Description:FIELD OF INVENTION
The present invention relates to a process for generating a mosaic image of a predefined area of interest. More particularly, the invention relates to an automated process for generating a mosaic image for a user-defined geographical region from satellite images.
BACKGROUND
The use of satellites for imaging has been increasing for many years alongside the ever-growing pace of science and technology. For over four decades, optical remote-sensing satellites have been continuously observing Earth, gathering extensive data covering large areas over long periods. These data serve various purposes, such as monitoring crop growth, tracking changes in land cover and land use, detecting deforestation, assessing ecosystem health, and evaluating water quality. The launch of Sentinel-2 satellites by the European Space Agency has notably enhanced the frequency of acquiring low-cost optical data of Earth. However, optical sensors rely on clear skies to capture usable images, and clouds can obstruct important changes or developments.
To improve data quality and usability and considering the nature of satellite image acquisition, a plurality of geospatial images from the satellite may be placed together to form composite mosaics of satellite images. Conventionally, such mosaic generation was done through manual selection of images by a user. The user would review all available images for an area of interest and choose images for inclusion in the mosaic based on the subjective analysis and identification of the geospatial images received from the satellite. It is understandable that the manual process may be time consuming and costly. Moreover, the human intervention and the manual process may lead to presence of radiometric distortions and other defects such as visible boundaries of the several geospatial image segments in the final mosaic.
Several attempts have been made in the past to overcome the problems associated with the manual mosaic image generation process and eradicate the human intervention. Several solutions have been introduced in the technical field for generating a mosaic image through automation in the process at different levels. However, the existing processes and systems have not been able to achieve a fully automated generation of mosaic image without or, with minimum human intervention. This may result in a compromised efficiency and operational effectiveness.
Therefore, there is a need for an automated process for generating a mosaic image which is capable of minimizing the human intervention and overcoming at least the aforementioned challenges associated with geo-spatial image mosaicking.
SUMMARY OF THE INVENTION
It is an object of the invention to provide a method for autonomously generating mosaic images for a predefined area of interest, preferably a user-defined geographical region.
It is another object of the invention to efficiently process images received from satellite with minimal human intervention.
It is yet another object of the invention to generate high quality composite mosaic image which is seamless with unnoticeable or invisible blended boundaries.
It is yet another object of the invention to minimize the effect of obstructions like clouds captured in the satellite images when generating the output mosaic image.
According to an embodiment, the invention discloses a method of generating a mosaic image. The method comprises steps of monitoring image data from one or more satellites, receiving plurality of images from the image data and storing in a database, accessing the plurality of images from the database, comparing segments of each of the plurality of images to a user-defined geographical region, and selecting at least one image comprising a segment matching a corresponding segment of the user-defined geographical region.
The image data from one or more satellites is continuously monitored. In an alternate embodiment, the image date from one or more satellites is monitored at predefined regular intervals, for example, once every 24 hours.
The step of receiving the plurality of images is initiated when the satellite image data is distinct in comparison to a previously accessed plurality of images, or the image data stored in the database is detected. The plurality of images to be received is identified based on predefined criteria selected from geographic coordinates, block level boundaries, district boundaries, state level boundaries and the like. The database where the downloaded plurality of images is stored may be a local database and/or a cloud database.

In an embodiment, comparison of the segments of the plurality of images to the user-defined geographical region is processed automatically. The automated comparison is performed through algorithms processed by a graphics processing unit and comprises optimizations. The optimization comprises parallel processing and adaptive resolution adjustments. A selected image comprises a high-quality segment in accordance with predefined image quality criteria. The predefined image quality criteria comprise parameters of resolution, optimal coverage, minimal obstructions, geometric accuracy, edge enhancement.
In an embodiment, automated radiometric and atmospheric corrections are applied to the plurality of images stored in the database before comparing the segments of images to the user-defined geographical region. Furthermore, each of the image stored in the database is aligned to a common coordinate system. the comparison and selection of images comprising segments corresponding to the user-defined geographical region is based on intersection of geo-coordinates of the segments of images and the geo-coordinates of the user-defined geographical region.
After the step of selecting the at least one image, segments are cropped from each of the plurality of selected images. The cropped segments from the plurality of images are merged to form a mosaic replicating an image of the user-defined geographical region. Furthermore, a spatial interpolation operation is applied on the mosaic to address gaps and missing data. The mosaic image is processed and optimized to generate the output mosaic image comprising blended boundaries of individual segments. The output mosaic image is seamless, and the blended boundaries are un-noticeable or invisible to the human eye. The user-defined geographical region comprises specific geographical areas of interest. The user-defined geographical regions may comprise regular, irregular, varying in size, free-form, circular, elliptical, polygonal areas. In an embodiment, geographic information systems (GIS) are integrated for advanced spatial data manipulation and visualization. The GIS integration helps manage, analyse, and visualize the data effectively, ensuring accurate and detailed final output mosaic images.

During initial image data collection and preprocessing, georeferencing is done and using GIS tools, user-specific geographical regions (regular, irregular, varying in size, free-form, circular, elliptical, polygonal) are defined. Spatial data manipulation is performed in relation to the images, such as, clipping, masking, and adjusting images to fit the defined regions. Thereafter, spatial analysis for evaluating image quality and consistency is also conducted. Database integration is done for efficiently storing, managing, and retrieving geospatial data.
These steps ensure that GIS integration helps manage, analyze, and visualize the data effectively, resulting in accurate and detailed final output mosaic images.
According to an embodiment, the invention comprises a system implementing one or more aspects of method steps disclosed in the present invention. The system comprises one or more satellites for capturing and providing high resolution images of the specific areas of earth. Each of the satellites comprising one or more cameras for capturing geo-spatial images. In other words, the satellites provide high resolution geo-spatial images. The system also comprises a monitoring unit configured to monitor image data captured by the one or more satellites and an image acquisition unit configured to selectively receive and/or download a plurality of images from the satellite mage data. The system further comprises a transmission unit configured to transmit the received plurality of images to a database, and a processing module configured to access the plurality of images stored in the database. The processor is configured to compare segments of each of the plurality of images with a user-defined geographical region. Based on comparison, the processor selects at least one image comprising a segment matching a corresponding segment of the user-defined geographical region.
The system further comprises a storage memory for storing data, preferably, computer readable instructions. The computer readable instructions may comprise artificial intelligence and machine learning based algorithms. The instructions are executed by a processing circuitry of the processing module.
In an embodiment, a machine learning model is trained using input data, preferably, data generated throughout the method steps of generating a mosaic image in accordance with the present invention.
The present invention provides the following and other advantages:
a) Optimizes resource utilization by extracting and processing only portions of tiles that intersect with the input polygon.
b) The selection of specific images containing relevant portions of the input image or pre-defined geographical region, this invention offers unparalleled scalability and flexibility.
c) Mosaic images accurately represent the underlying terrain and features of interest.
d) Revolutionizing the mosaic generation process through algorithmic efficiency and workflow optimization.
e) Mosaic image generation with improved data quality, usability and creating a composite mosaic image that enable users, including but not limiting to farmers, agricultural stakeholders, geologists, weather forecast professional, etc., to access clear and detailed visuals of respective geographical regions, even in the presence of cloud cover.
f) The satellite mosaic images have several uses, applications, and advantages in a variety of sectors like crop monitoring, irrigation management, land use planning, and environmental monitoring, etc.

BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a flowchart describing steps of a method of generating a mosaic image automatically.
Figure 2 illustrates a schematic diagram of a system for automated generation of a mosaic image.
Figures 3a-3d illustrate examples of output mosaic image generated from multiple images corresponding to a pre-defined area of interest according to an embodiment of the invention.
Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.
DETAILED DESCRIPTION
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, persons skilled in the art will recognize that various changes and modifications to the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
The terms and words used in the following description are not limited to the bibliographical meanings and are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to the person skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only.
According to a general aspect, the present invention aims at creating and/or providing an automated method and system for the image mosaic generation from a plurality of images received from one or more satellites. In particular, the system continuously monitors image data of one or more satellites and receives satellite image data, preferably, once every 24 hours. The system automatically accesses and processes the image data comprising plurality of images to generate high-quality composite mosaic images based on user-defined input images. The user-defined images include geographical region for a given area of interest.
In an embodiment, the mosaic image may be generated from a plurality of geospatial images acquired using Earth-orbiting satellites, such as the ESA's Sentinel-2 satellites, which are part of the Copernicus program. This program aims to provide high-resolution imagery for various purposes, including environmental monitoring, agricultural analysis, food security, and other applications. . The images selected for incorporation in a mosaic may consist of pre-processed geospatial images (e.g., automated radiometric and atmospheric corrections are applied to the images along with geometric corrections to align the images to a common coordinate system).
In this regard, an output mosaic image composed of geospatial images may be generated with automatic image selection, eliminating the need for human operator to manually choose images for mosaic. In addition, the present invention describes a process that automatically crops images and merges plurality of images.
As may be appreciated, for any or all aspects described herein, specific processing techniques may also be provided that allow the generation of the mosaic to occur in a relatively short time, thus effectively utilizing computational algorithms. In turn, specific embodiments of the present invention are described in detail herein in relation to the automatic generation of a mosaic image.
Figure 1 shows a flowchart describing a method of generating a mosaic image according to an embodiment of the present invention.
In an embodiment, the method of Figure 1 is implemented by a system 200 as shown in Figure 2 and the system 200 enables the method for automated generation of a mosaic image. The system 200 comprises one or more satellites 201 for providing high resolution images of specific areas of earth 210, a plurality of satellite imagery tiles 220 in the form at least one image tile as a part of satellite image data. The plurality of satellite imagery tiles 220 comprises at least one image tile 220-1, 220-2, 220-3, 220-4. The system 200 also comprise a satellite data processing device 230 and an output mosaic image 240. The processing device(s) 230 may include a processing module 230-1 and a storage memory 230-2. The system 200 further comprises a database 250.
The system 200 comprises a monitoring unit (not shown) for continuously monitoring the image data captured through satellite 201. The system 200 further comprises an image acquisition unit (not shown) configured to acquire a plurality of images from the image data of the satellite 201. A plurality of images from the image data of satellite 201 is received by the image acquisition unit when the satellite image data is distinct in comparison to a previously accessed plurality of images, or the image data stored in the database is detected by the monitoring unit. The system 200 also comprises a transmission unit (not shown) configured to transmit the plurality of images received by the image acquisition unit to the database 250. The plurality of images stored in the database 250 are accessed by the processing module 230-1. In an embodiment, the processing module 230-1 comprises one or more processors, including but not limited to, microcontrollers, microprocessors.
The method of generating the output mosaic image 240, according to the flowchart shown in Figure 1, may be initiated by providing inputs related to geographical coordinates corresponding to a user-defined area of interest (a geographical region) as an input in step 110. It may be noted that a pre-defined user created image of a geographical area of interest (AOI) may also be provided as an input in step 110, termed as input AOI image. The geographical coordinates define the boundaries of the geographical region for which the mosaic image needs to be created.
In step 120, the processing module 230-1 accesses the plurality of images stored in the database 250, identifies and determines the best quality segments from the plurality of images. The processing module 230-1 compares image segments of the plurality of image tiles to a base layer using advanced artificial intelligence and machine learning algorithms, focusing on user-defined areas of interest, ensuring optimal coverage and minimal obstructions. In an embodiment, the base layer may comprise the user-defined area of interest or the geographical region image provided as an input in step 110.
Satellite images are typically divided into tiles (like a grid) to cover large areas. The processing module, based on the above comparison, identifies the image tiles overlapping with the input image of user-defined geographical area of interest (AOI). Also, the system 200 uses network and buffer analysis techniques to optimize image tile connections, reducing computational load and accelerating the mosaic image generation process. Here, the specific satellite image tiles that cover parts of the input AOI image are identified. This involves spatial analysis to ensure that all tiles containing any portion of the AOI image are selected for further processing. Also, spatial autocorrelation assesses and maintains similarity between spatially proximate observations to ensure smooth blending of adjacent image segments.
In step 130, based on comparison metrics, from identified satellite image tiles (obtained from step 120), only the relevant segments comprising highest quality segments that fall within the boundaries of the input AOI image are extracted. This means cropping the image tiles to only include the portions that are within the area of interest, reducing the data size, and focusing on the required information. The cropping process involves cutting out the parts of each identified image tiles 220-1, 220-1, 220-3, 220-4 that correspond to the input AOI image. This step ensures that only the necessary data is used, which makes the subsequent steps more efficient.
In an embodiment, the process of extracting relevant image segments for generating mosaic image from multiple satellite image tiles, includes identification and delineation of regions of interest (ROIs) within the satellite images. Utilizing advanced algorithms and artificial intelligence/machine learning programs, the system automatically segments the satellite image tiles based on input AOI image. This ensures that only the pertinent areas are included in the final mosaic, enhancing the efficiency and accuracy of the mosaic generation process.
Furthermore, in step 140, the cropped segments from different tiles are then combined. This involves aligning and merging these segments to form a continuous image that covers the entire area of interest. The goal is to create a seamless mosaic where the boundaries between different segments are un-noticeable or invisible to human eyes.
The present invention in specific embodiments autonomously manages the cropping and integration of image segments, guided by the spatial boundaries defined by user input polygons. This involves systematically cropping relevant sections of the satellite images and integrating them into a unified mosaic. The process ensures spatial accuracy and consistency across the entire mosaic. By automating these steps, the invention eliminates the need for manual intervention, significantly streamlining the image processing workflow and enabling the efficient production of high-quality mosaic images.
In step 150, the combined segments are processed to generate the output mosaic image 240 as shown in Figure 2. This step includes any final adjustments, such as color correction, blending, and optimization, to ensure that the mosaic image is of high quality and accurately represents the AOI.
In a preferred embodiment of the invention, in order to address gaps or missing data within the mosaic, spatial interpolation techniques are employed. This involves estimating and generating image data for areas that might be obscured by clouds or other obstructions. By interpolating values between known data points, the system ensures a more complete and seamless final mosaic image. This technique is crucial for maintaining the integrity and continuity of the mosaic, particularly in regions where continuous observation is hindered by environmental factors. An example for spatial interpolation comprising the required calculations is provided below:
Example 1
Using the known data points around Aligarh, Uttar Pradesh, the estimated value at the new point (27.87, 78.11) using Inverse Distance Weighting (IDW) is approximately 20.87.
Calculation Steps:
1. Known Data Points:
? Point A: (27.88, 78.08) with value 15
? Point B: (27.92, 78.10) with value 20
? Point C: (27.85, 78.12) with value 25
? Point D: (27.80, 78.09) with value 10
2. New Point for Estimation:
? New Point: (27.87, 78.11)
3. Distance Calculation:
? Distance from A: sqrt{(27.88 - 27.87)^2 + (78.08 - 78.11)^2} = 0.0316
? Distance from B: sqrt{(27.92 - 27.87)^2 + (78.10 - 78.11)^2} = 0.0501
? Distance from C: sqrt{(27.85 - 27.87)^2 + (78.12 - 78.11)^2} = 0.0224
? Distance from D: sqrt{(27.80 - 27.87)^2 + (78.09 - 78.11)^2} = 0.0707
4. Weights Calculation (with p = 2):
? Weight for A: 1/(0.0316^2)= 1000
? Weight for B: 1/(0.0501^2)= 398
? Weight for C: 1/(0.0224^2) = 1992
? Weight for D: 1/(0.0707^2) = 200
5. Weighted Values:
? Weighted value for A: 15×1000 = 15000
? Weighted value for B: 20×398 = 7960
? Weighted value for C: 25×1992 = 49800
? Weighted value for D: 10×200 = 2000
Summation:
? Numerator: 15000+7960+49800+2000 = 74760
? Denominator: 1000+398+1992+200 = 3590
Estimated Value:
? Z^(27.87,78.11)=3590/74760 = 20.87
The given estimated value in above example derived using spatial interpolation is used to address gaps and missing data, ensuring the final mosaic image is seamless and complete.
Furthermore, in step 160, the final mosaic image is produced. This image is a high-fidelity representation of the specified region, created by combining the relevant segments from multiple satellite image tiles. The final produced mosaic image is used in various applications, such as monitoring crop growth, track changes in land cover, or assessing ecosystem health. In this context an area of interest may be a field or a region of a district containing the crops.
In an embodiment, the storage memory 230-2 may comprise artificial intelligence and machine learning based instructions in the form of computer programs and/or algorithms. The instructions are executed by the processing module 230-1 to perform the method steps of the present invention, or at least the steps 130-160 as described above.
Such automated processing of method steps may be performed by a machine learning model. The Said machine learning model is trained through data stored in one or more storage memories. The processing module 230-1 comprises at least one processor. The said processor can be either a single core or multi-core processor.
In an embodiment, the processing module/processor may include a central processing unit (CPU), which can efficiently handle the execution of instructions and tasks and an application-specific integrated circuit (ASIC), which can optimize the performance for a particular function. The processing module/processor may further comprise an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction-set computer (RISC), a microprocessor, or the like, or any combination thereof.
Furthermore, the storage memory 230-1 may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM), or the like, or any combination thereof.
The database 250 may store high resolution and low-resolution images, cropped images and the final mosaic image 240. The database 250 is designed to efficiently manage large volumes of data, allowing for quick retrieval and processing. In an embodiment, the plurality of images received from satellite 201 and stored in database 250 are aligned to a common coordinate system. Aligning the plurality of images stored in the database 250 to a common coordinate system is crucial for ensuring that all images share the same geographic reference framework. This process, often referred to as georeferencing, involves assigning real-world coordinates to each pixel of the image, ensuring that all images align correctly when combined or compared.
Each image in the database is processed to match a specific coordinate system, such as WGS84 (World Geodetic System 1984). This involves transforming the image's spatial data so that its geographic location corresponds accurately with the chosen coordinate system. When images are aligned to a common coordinate system, comparing them becomes straightforward. Analysts can easily identify changes over time, assess spatial relationships, and perform various geospatial analyses with confidence that the data is accurately aligned. Aligning images to a common coordinate system simplifies the processing workflow. Automated algorithms can efficiently handle and process the images without needing to account for varying coordinate systems, enhancing the overall efficiency and reliability of the system. Aligned images provide a clear and accurate visual representation of geographic areas. This enhances the interpretability of the mosaic, making it easier for users to identify features, assess conditions, and derive insights from the data. By aligning images in the database to a common coordinate system, our invention ensures that all spatial data is accurately referenced, facilitating the creation of precise and reliable image mosaics. This enhances the overall functionality, accuracy, and usability of the system, making it a powerful tool for various geospatial applications.
According to an embodiment, information received and/or exchanged by the processing device(s) 230, from satellite imagery tiles 200, and database 250 may be via wired or wireless network (not shown). For example, the network may include a cable network, a wireline network, an optical fiber network, a tele communications network, an intranet, an Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN) or the like, or any combination thereof. The present invention represents a revolutionary paradigm shift in satellite image processing, transcending traditional methodologies by its integration of automated mosaic generation protocols with refined algorithmic frameworks. By synergistically harnessing computational algorithms with spatial analysis techniques, the invention efficiently transforms fragmented satellite image data into cohesive, high-fidelity mosaic representations of target regions.
Figures 3a-3d illustrate examples of output mosaic image generated from multiple images corresponding to a pre-defined area of interest according to an embodiment of the invention.
In an embodiment, the present invention operates as a bespoke software system tailored to the intricate demands of satellite image analysis and mosaic image generation. It meticulously orchestrates the aggregation of relevant image segments from disparate tiles, guided by the spatial boundaries delineated by user-defined input polygons. This fusion of computational intelligence and spatial data processing confers unprecedented efficiency, accuracy, and versatility to the mosaic generation process, thereby revolutionizing the landscape of satellite imagery utilization across diverse sectors.
The present invention in specific embodiments is directed to enhance efficiency by autonomously managing the entire image processing workflow, from downloading daily satellite image data to generating the final mosaic images. This automation significantly reduces the need for manual intervention, which not only saves time and effort but also minimizes the risk of human error. By streamlining the image processing steps, the system can consistently produce high-quality mosaic images with greater speed and reliability, enhancing overall operational efficiency.
To enhance the accuracy of the mosaic image the system uses advanced computational algorithms that precisely handle the segmentation, cropping, and integration of image data. These refined algorithms ensure that only the relevant image segments are included in the final mosaic, based on predefined input polygons. This precise handling of image data improves the accuracy of the spatial delineation and integration processes, resulting in high-fidelity mosaic images. The algorithm’s sophistication ensures that the final product is both detailed and accurate, meeting the high standards required for various applications.
For cloud and obstruction management, the system employs spatial interpolation techniques to address gaps and missing data caused by clouds or other obstructions. By estimating and generating image data for obscured areas, the system ensures that the final mosaic is complete and seamless. This feature is crucial for maintaining the continuity and integrity of the mosaic, especially in regions with frequent cloud cover. The ability to effectively manage and compensate for environmental factors that obscure satellite imagery ensures that the mosaics are reliable and usable regardless of weather conditions.
For efficient data processing, network analysis and buffer analysis techniques are used to optimize the pathways and connections between different image tiles, ensuring efficient data processing. This optimization reduces the computational load and accelerates the mosaic generation process. By strategically managing how image tiles are processed and integrated, the system can handle large volumes of data more efficiently, leading to faster production times and reduced resource consumption. This optimization is key to the system's ability to produce high-quality mosaics quickly and efficiently.
The ability to adjust the resolution of input data or intermediate results is based on factors such as processing load, available memory, or the specific requirements of algorithms being applied. Adaptive resolution adjustment helps in managing computational resources effectively. It allows the system to balance between processing speed and accuracy, optimizing both time and energy consumption. Overall, adaptive resolution adjustment enhances efficiency in data processing by dynamically altering resolution levels to match the computational demands and constraints of the task at hand, thereby improving overall system performance and responsiveness.
In a preferred embodiment, the invention constitutes an advancement within the realm of process innovation, epitomizing a pioneering approach to satellite image mosaic generation that transcends conventional limitations. It represents a symbiosis of software sophistication, algorithmic prowess, and spatial intelligence, culminating in a transformative solution that optimizes resource utilization, enhances operational efficiency, and elevates the fidelity and utility of resultant mosaic imagery. In essence, the invention not only introduces a novel methodology for satellite image processing but also pioneers a paradigmatic shift in the fusion of computational and spatial sciences, thereby enriching the arsenal of tools available for remote sensing applications.
The invention distinguishes itself from present technology through its innovative approach to automating the mosaic generation process and its utilization of input AOI spanning multiple satellite image tiles. This method entails identifying specific regions within each tile that correspond to the input AOI and subsequently cropping those regions. By iteratively processing multiple tiles containing portions of the input AOI, the invention systematically combines these cropped segments to create a comprehensive mosaic image, obviating the need to assemble all tiles simultaneously.
In the preferred embodiment, the present invention addresses several critical challenges prevalent in contemporary satellite image processing techniques:
Efficiency and Resource Optimization:
Traditional methods often require the simultaneous retrieval and processing of all satellite image tiles, irrespective of their relevance to the target area. In contrast, the present invention optimizes resource utilization by selectively extracting and processing only those portions of tiles that intersect with the input AOI. This streamlined approach minimizes computational overhead and accelerates mosaic generation, enhancing operational efficiency.
Scalability and Flexibility:
By facilitating the selection of specific tiles containing relevant portions of the input AOI, the present invention offers unparalleled scalability and flexibility. Users can seamlessly accommodate varying user-defined geographical region with varied sizes and configurations without compromising processing efficiency or accuracy. This flexibility empowers users to tailor mosaic generation workflows to their unique requirements, accommodating diverse applications and spatial extents with ease.
Accuracy and Precision:
The precision afforded by present invention ensures that mosaic images accurately represent the underlying terrain and features of interest. By precisely delineating and cropping regions within each tile corresponding to the input AOI, it minimizes spatial distortions and alignment errors, yielding mosaic images of exceptional fidelity and accuracy. This enhanced precision is particularly advantageous in applications requiring high-resolution imagery for detailed analysis and decision-making.
Automation and Streamlining:
Automation lies at the core of invention, revolutionizing the mosaic generation process through algorithmic efficiency and workflow optimization. By automating the identification, extraction, and merging of relevant tile segments, this technology significantly reduces manual intervention and accelerates the generation of mosaic images. The present invention not only streamlines operational workflows but also mitigates the risk of human error, enhancing the reliability and consistency of output products.
The present invention further offers distinct advantages over traditional methods of monitoring agricultural health such as:
a) Providing visually informative images that highlight subtle variations in crop health and environmental conditions,
b) Empowers users to make data-driven decisions regarding irrigation needs, nutrient deficiencies, and pest outbreaks.
c) Agricultural management not only enhances crop yields and reduces input costs but also promotes sustainable farming practices.
In an embodiment, the present invention holds significant potential for various applications, with its major focus on revolutionizing agricultural monitoring and management practices. The primary application of the invention lies in precision agriculture, where it enables farmers and agricultural stakeholders to monitor crop health, assess environmental conditions, and make informed decisions to optimize productivity and resource utilization.
In a preferred embodiment of the present invention, the satellite image mosaic process can be used for:
Crop Monitoring: Farmers can utilize the detailed mosaic images generated by the invention to monitor crop health, identify areas of stress or nutrient deficiencies, and take timely corrective actions such as adjusting irrigation schedules or applying targeted fertilization.
Irrigation Management: By visually assessing moisture levels in the soil and detecting signs of water stress in crops, farmers can optimize irrigation practices and ensure efficient water use, thereby conserving water resources and reducing irrigation costs.
Land Use Planning: Agricultural organizations and land managers can use the detailed mosaic images to assess land suitability, plan crop rotations, and optimize land use practices for sustainable agriculture and environmental conservation.
Environmental Monitoring: Beyond agriculture, the invention can be applied to environmental monitoring initiatives, such as assessing deforestation, monitoring habitat changes, and tracking environmental changes over time. Overall, the invention's major applications centre on empowering stakeholders in agriculture and environmental management with detailed, urban planning, visually informative data to support decision-making, drive efficiency, and promote sustainability.
The process for satellite image mosaic generation according to present invention, constitutes a pioneering advancement in the field of remote sensing technology. It amalgamates elements of process innovation, software development, and algorithmic refinement to automate and optimize the assembly of composite mosaic images from satellite-derived data. This innovative process entails the systematic extraction, cropping, and integration of pertinent image segments originating from multiple satellite image tiles, facilitated by the delineation of input areas of interest.
The present disclosure may have additional embodiments, which may be practiced without one or more of the details described for any particular described embodiment, or may have any detail described for one particular embodiment practiced with any other detail described for another embodiment. Furthermore, while certain embodiments have been illustrated and described, as noted above, many changes can be made without departing from the spirit and scope of the disclosure.
Use of “one or more” or “at least one” or “a” is intended to include one or a plurality of the element referenced. Reference to an element in singular form is not intended to mean only one of the element and does include instances where there are more than one of an element unless context dictates otherwise. Use of the term ‘and’ or ‘or’ is intended to mean ‘and/or’ unless context dictates otherwise.

We Claim:
1. A method of generating a mosaic image, comprising:
monitoring image data from one or more satellites;
receiving plurality of images from the image data and storing in a database;
accessing the plurality of images from the database;
comparing segments of each of the plurality of images with a user-defined geographical region; and
selecting at least one image comprising a segment matching a corresponding segment of the user-defined geographical region.

2. The method as claimed in claim 1, wherein the image data from one or more satellites is continuously monitored.

3. The method as claimed in claim 1, wherein the image data from one or more satellites is monitored on predefined regular intervals.

4. The method as claimed in claim 1, wherein the step of receiving the plurality of images is initiated when the plurality of images is distinct from a stored image data in the database.

5. The method as claimed in claim 1, wherein the plurality of images to be received is identified based on predefined criteria selected from geographic coordinates, block level boundaries, district boundaries, block Level Boundaries, state level boundaries and country level boundaries.

6. The method as claimed in claim 1, wherein the database is a local database and/or a cloud database.

7. The method as claimed in claim 1, wherein the comparison of segments of the plurality of images to the user-defined geographical region is automated.

8. The method as claimed in claim 7, wherein the automated comparison is performed through algorithms processed by a graphics processing unit and comprises optimizations.

9. The method as claimed in claim 8, wherein the optimizations comprise parallel processing and adaptive resolution adjustments.

10. The method as claimed in claim 7, wherein a selected image comprises a high-quality segment according to a predefined image quality criteria.

11. The method as claimed in claim 10, wherein the predefined image quality criteria comprises parameters of resolution, optimal coverage, minimal obstructions, geometric accuracy and edge enhancement.

12. The method as claimed in claim 1, wherein automated radiometric and atmospheric correction are applied to the plurality of images stored in the database before comparing the segments of images to the user-defined geographical region.

13. The method as claimed in claim 12, wherein the each of the image stored in the database is aligned to a common coordinate system.

14. The method as claimed in claim 12, wherein the comparison and selection of images comprising segments corresponding to the user-defined geographical region is based on intersection of geo-coordinates of the segments of images and the geo-coordinates of the user-defined geographical region.

15. The method as claimed in claim 1, wherein segments are cropped from each of the plurality of selected images.

16. The method as claimed in claim 15, wherein the cropped segments are merged to form a mosaic replicating an image of the user-defined geographical region.

17. The method as claimed in claim 16, wherein a spatial interpolation operation is applied on the mosaic to address gaps and missing data.

18. The method as claimed in claim 16, wherein the mosaic is processed and optimized to generate the output mosaic image comprising blended boundaries of individual segments.

19. The method as claimed in claim 18, wherein the output mosaic image is seamless, and the blended boundaries are un-noticeable to the human eye.

20. The method as claimed in claim 1, wherein the user-defined geographical region comprises specific geographical areas of interest.

21. The method as claimed in claim 20, wherein the user-defined geographical regions may comprise regular, irregular, varying in size, free-form, circular, elliptical, polygonal areas.

22. The method as claimed in claim 1, wherein geographic information systems are integrated for advanced spatial data manipulation and visualization.

23. A system for generating a mosaic image, comprising:
one or more satellites for capturing a plurality of images;
a monitoring unit configured to monitor image data captured by the one or more satellites;
an image acquisition unit configured to selectively receive the plurality of images from the satellite;
a transmission unit configured to transmit the data to a database;
a processing module configured to access the plurality of images stored in the database, comparing segments of each of the plurality of images with a user-defined geographical region, and selecting at least one image comprising a segment matching a corresponding segment of the user-defined geographical region.

24. The system as claimed in claim 23, wherein the system comprises a storage memory for storing data, preferably, computer readable instructions.

25. The system as claimed in claim 24, wherein the computer readable instructions may comprise artificial intelligence and machine learning based algorithms.

26. The system as claimed in claim 24, wherein the instructions are executed by a processing circuitry of the processing module.

27. The system as claimed in claim 26, wherein a machine learning model is trained using input data, preferably, data generated by the method of any of claims 1 to 22.

28. The system as claimed in claim 23, wherein the each of the satellites comprising one or more cameras for capturing geo-spatial images.

ABSTRACT
A Method and System for Generating A Mosaic Image
Described herein is a method and system for generating a mosaic image. The method comprises monitoring image data from one or more satellites, receiving plurality of images from the image data and storing in a database, accessing the plurality of images from the database, comparing segments of each of the plurality of images with a user-defined geographical region, and selecting at least one image comprising a segment matching a corresponding segment of the user-defined geographical region. The method of present invention is implemented by a system comprising a machine learning model.
Refer Figure 1
, C , Claims:We Claim:
1. A method of generating a mosaic image, comprising:
monitoring image data from one or more satellites;
receiving plurality of images from the image data and storing in a database;
accessing the plurality of images from the database;
comparing segments of each of the plurality of images with a user-defined geographical region; and
selecting at least one image comprising a segment matching a corresponding segment of the user-defined geographical region.

2. The method as claimed in claim 1, wherein the image data from one or more satellites is continuously monitored.

3. The method as claimed in claim 1, wherein the image data from one or more satellites is monitored on predefined regular intervals.

4. The method as claimed in claim 1, wherein the step of receiving the plurality of images is initiated when the plurality of images is distinct from a stored image data in the database.

5. The method as claimed in claim 1, wherein the plurality of images to be received is identified based on predefined criteria selected from geographic coordinates, block level boundaries, district boundaries, block Level Boundaries, state level boundaries and country level boundaries.

6. The method as claimed in claim 1, wherein the database is a local database and/or a cloud database.

7. The method as claimed in claim 1, wherein the comparison of segments of the plurality of images to the user-defined geographical region is automated.

8. The method as claimed in claim 7, wherein the automated comparison is performed through algorithms processed by a graphics processing unit and comprises optimizations.

9. The method as claimed in claim 8, wherein the optimizations comprise parallel processing and adaptive resolution adjustments.

10. The method as claimed in claim 7, wherein a selected image comprises a high-quality segment according to a predefined image quality criteria.

11. The method as claimed in claim 10, wherein the predefined image quality criteria comprises parameters of resolution, optimal coverage, minimal obstructions, geometric accuracy and edge enhancement.

12. The method as claimed in claim 1, wherein automated radiometric and atmospheric correction are applied to the plurality of images stored in the database before comparing the segments of images to the user-defined geographical region.

13. The method as claimed in claim 12, wherein the each of the image stored in the database is aligned to a common coordinate system.

14. The method as claimed in claim 12, wherein the comparison and selection of images comprising segments corresponding to the user-defined geographical region is based on intersection of geo-coordinates of the segments of images and the geo-coordinates of the user-defined geographical region.

15. The method as claimed in claim 1, wherein segments are cropped from each of the plurality of selected images.

16. The method as claimed in claim 15, wherein the cropped segments are merged to form a mosaic replicating an image of the user-defined geographical region.

17. The method as claimed in claim 16, wherein a spatial interpolation operation is applied on the mosaic to address gaps and missing data.

18. The method as claimed in claim 16, wherein the mosaic is processed and optimized to generate the output mosaic image comprising blended boundaries of individual segments.

19. The method as claimed in claim 18, wherein the output mosaic image is seamless, and the blended boundaries are un-noticeable to the human eye.

20. The method as claimed in claim 1, wherein the user-defined geographical region comprises specific geographical areas of interest.

21. The method as claimed in claim 20, wherein the user-defined geographical regions may comprise regular, irregular, varying in size, free-form, circular, elliptical, polygonal areas.

22. The method as claimed in claim 1, wherein geographic information systems are integrated for advanced spatial data manipulation and visualization.

23. A system for generating a mosaic image, comprising:
one or more satellites for capturing a plurality of images;
a monitoring unit configured to monitor image data captured by the one or more satellites;
an image acquisition unit configured to selectively receive the plurality of images from the satellite;
a transmission unit configured to transmit the data to a database;
a processing module configured to access the plurality of images stored in the database, comparing segments of each of the plurality of images with a user-defined geographical region, and selecting at least one image comprising a segment matching a corresponding segment of the user-defined geographical region.

24. The system as claimed in claim 23, wherein the system comprises a storage memory for storing data, preferably, computer readable instructions.

25. The system as claimed in claim 24, wherein the computer readable instructions may comprise artificial intelligence and machine learning based algorithms.

26. The system as claimed in claim 24, wherein the instructions are executed by a processing circuitry of the processing module.

27. The system as claimed in claim 26, wherein a machine learning model is trained using input data, preferably, data generated by the method of any of claims 1 to 22.

28. The system as claimed in claim 23, wherein the each of the satellites comprising one or more cameras for capturing geo-spatial images.