Field of the Invention
[0001] The present invention relates generally to the field of suppression of spectral response of vegetation. More particularly, the present invention relates to a system and a method for suppression of spectral response of vegetation in multispectral image data for a densely vegetated area.
Background of the Invention
[0002] Many industries and academic research benefit from technology to identify growth rate and conditions of vegetation in vegetated areas. A large number of remote sensing methods have been developed to quantify vegetation conditions. Satellite, air and terrestrial, including hand-held, remote sensing systems may provide useful information for such purposes. In particular, imaging methods have been developed in which an area of vegetation is imaged in multiple spectral bands. Many indices have been developed that take advantage of spectral reflectance properties of chlorophyll in order to differentiate vegetation from non-vegetation. Chlorophyll has a relatively high reflectance in near infrared and a relatively low reflectance in visible portions of spectrum and in a red spectral range of the visible spectrum. Most other common materials found in vicinity of vegetation, such as soil, rocks, and water, have variable reflectances in near infrared and visible spectral regions, depending upon their chemical composition.
[0003] Conventionally, for objectives aimed at characterizing and quantifying vegetation species, it has been observed that the spectral response of the vegetation and relationship among spectral response values in different spectral bands, called vegetation indices, are generally employed. Further, typically, while for areas having single species of vegetation, e.g. grasslands or farms, simple vegetation indices (such as a ratio of near-infrared band and red band spectral values)are used, it

has been observed that for more complex natural environments, having a range of vegetation species, such simple vegetation indices do not yield satisfactory results. Furthermore, for objectives aimed at measuring and characterizing spectral response of soils and rocks covered by layers of living vegetation (i.e., photosynthetically active chlorophyll-rich leafy plants) in multispectral image data, the spectral response and inter¬relationships (indices) underlying vegetated areas are presently only partially applicable and limited for sparsely vegetated areas only. Yet further, it has been observed that for thickly vegetated regions having layered vegetation, such as a jungle, forest, or even plantations having high vegetation densities, spectral characterization of underlying rocks or soils is much less accurate since solar illumination does not penetrate deeply enough into the multiple layers of vegetation to provide an accurate response of geologic materials such as minerals, rocks and soils.
[0004] Further, in passive remote sensing techniques, electromagnetic energy of the sun passes through the atmosphere to the target and back through atmosphere to remote sensing satellite sensors. Radiation from the earth's surface undergoes significant interaction with the atmosphere before it reaches the satellite sensors. In this scenario, generally, digital image pre¬processing techniques of atmospheric correction are used for detection of unique materials in space-borne remotely sensed data. However, it has been observed that the conventional techniques require detailed knowledge of image acquisition geometry and geographic location of the image area, which is a tedious and time-consuming process involving huge computational overhead.
[0005] Further, among conventional digital image processing techniques developed to mitigate vegetation response in multispectral satellite data, forced invariance method is used to suppress vegetation contribution to a mixed image pixel of an input multispectral image of an area of interest. However, it has been observed that the current technologies for forced invariance are applicable only for areas with less than 50% vegetation cover. The forced invariance technologies essentially require estimation of vegetation abundance based on a simple vegetation index i.e. use of a simple band ratio. As indicated above, a simple band

ratio is deemed inaccurate for densely vegetated areas. Also, in the forced invariance method, atmospheric correction is performed on the input satellite multispectral image data using dark pixel correction approach, which requires assumed presence of spectrally dark pixels in the input image for atmospheric correction. Typically, dark pixel correction assumes that there is no spectral response coming from shaded areas or from water bodies as these areas absorb all the radiation. However, it has been observed that many input images may actually not have dark pixels or the apparently dark pixels may not be completely devoid of any spectral response, and therefore results may not be accurate.
[0006] In light of the aforementioned drawbacks, there is a need for a system and a method for efficient and accurate suppression of multispectral vegetation response in densely vegetated areas where the spectral response of geologic materials is of primary interest.
Summary of the Invention
[0007] In various embodiments of the present invention, a
system for suppression of multispectral response of vegetation in
a multispectral image is provided. The system comprises a memory
for storing program instructions and a processor for executing
program instructions stored in the memory. The system comprises a
computation engine executed by the processor and configured to
receive a digital multispectral image data comprising a plurality
of digital numbers. The digital numbers represent pixel values of
a plurality of bands of an electromagnetic spectrum reflected from
a dense vegetated area. The configuration engine is configured to
apply an atmospheric correction using a radiative transfer physics
based principle over the digital numbers to generate an
atmospherically corrected multispectral data. The computation
engine is configured to compute
a normalized difference vegetation index (NDVI) values from the
atmospherically corrected multispectral data. The NDVI values are
in between -1 and +1. The
configuration engine is configured to generate a scatter plot between the atmospherically corrected multispectral data and the NDVI values. The scatter plot is used to generate a best fit curve

by using regression analysis. The configuration engine is configured to compute new pixel values based on the best fit curve and generate a vegetation suppressed image from the new pixel values.
[0008] In various embodiments of the present invention, a method for suppression of multispectral response of vegetation in a multispectral image is provided. The method comprises receiving a digital multispectral image data comprising a plurality of digital numbers. The digital numbers represent pixel values of a plurality of bands of an electromagnetic spectrum reflected from a dense vegetated area. The method comprises applying an atmospheric correction using pre-existing radiative transfer physics based principle over the digital numbers to generate an atmospherically corrected multispectral data. The method comprises computing a normalized difference vegetation index (NDVI) from the atmospherically corrected multispectral data. The NDVI values are in between -1 and +1. The method comprises generating a scatter plot between the atmospherically corrected multispectral data and the NDVI values. The scatter plot is used to generate a best fit curve by using regression analysis. The method comprises computing new pixel values based on the best fit curve and generating a vegetation suppressed image from the new pixel values.
Brief description of the accompanying drawings
[0009] The present invention is described by way of embodiments illustrated in the accompanying drawings wherein:
[0010] FIG. 1 is a detailed block diagram of a vegetation suppression system, in accordance with an embodiment of the present invention;
[0011] FIG. 2 is a flowchart illustrating a method for vegetation suppression, in accordance with an embodiment of the present invention;

[0012] FIG. 3 shows a multispectral image of an area of interest, in accordance with an embodiment of the present invention;
[0013] FIG. 4 shows evaluation of the best fit curve, in accordance with an embodiment of the present invention;
[0014] FIG. 5 illustrates a vegetation suppressed image, in accordance with an embodiment of the present invention; and
[0015] FIG. 6 illustrates an exemplary computer system in which various embodiments of the present invention may be implemented.
Detailed description of the invention
[0016] The disclosure is provided in order to enable a person having ordinary skill in the art to practice the invention. Exemplary embodiments herein are provided only for illustrative purposes and various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. The terminology and phraseology used herein is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed herein. For purposes of clarity, details relating to technical material that is known in the technical fields related to the invention have been briefly described or omitted so as not to unnecessarily obscure the present invention.
[0017] The present invention would now be discussed in context of embodiments as illustrated in the accompanying drawings.
[0018] FIG. 1 illustrates a block diagram of a system 100 for vegetation suppression, in accordance with an embodiment of the present invention. The system 100 comprises a receiver unit 106, a computation subsystem 102 and an output unit 118. The computation

subsystem 102 comprises a computation engine 104, a processor 120 and a memory 122. The computation engine 104 further comprises an atmospheric data correction unit 108, a vegetation index computation unit 110, a scatter plot unit 114, a band statistics computation unit 112 and a pixel computation unit 116. In an exemplary embodiment of the present invention, the computation engine 104 may be implemented in a cloud computing architecture in which data, applications, services, and other resources are stored and delivered through shared data-centers. The computation engine 104 has multiple units that are configured to work in conjunction with each other with respect to the suppression of the vegetation. The various units of the computation engine 104 are operated via the processor 120 specifically programmed to execute instructions stored in the memory 122.
[0019] In an embodiment of the present invention, the computation engine 104 communicates with the receiver unit 106 and with the output unit 118 via a communication channel (not shown). The communication channel (not shown) may include, but is not limited to, a physical transmission medium, such as, a wire, or a logical connection over a multiplexed medium, such as, a radio channel in telecommunications and computer networking. The examples of radio channel in telecommunications and computer networking may include, but are not limited to, a local area network (LAN), a metropolitan area network (MAN) and a wide area network (WAN).
[0020] In an embodiment of the present invention, the receiver unit 106 is configured to receive an analog multispectral image captured by plurality of sensors in a satellite by remote sensing. The analog multispectral image captured by the satellite represents image of the area of interest and atmospheric conditions existing at the time of the image acquisition by the sensors of the satellite. The analog multispectral image captured by the satellite represents physical geography of the area of interest and light reflected from the vegetation and different natural or man-made materials and features in the area of interest. In an exemplary embodiment of the present invention, the analog multispectral image captured by the receiver unit 106 may be a standard false colour composite (FCC) image. In standard false

colour composite images, a vegetated area on the earth appears in different shades of red depending on the types and conditions of vegetation in the vegetated area, since chlorophyll-rich vegetation has a high reflectance in the Near Infrared (NIR) band. In this case, plants dominantly reflect in near infrared and green regions of the solar spectrum, while absorbing most in red range of the visible spectrum. Since the chlorophyll-rich plants reflect more near infrared than green, plant-covered land appears deeper red. Further, clear water absorbs all three wavelengths i.e. near infrared, red and green (partially), so it generally appears black. In an example, FIG. 3 illustrates the multispectral image showing the vegetation response in red and clear-water areas in black. In an exemplary embodiment of the present invention, the receiver unit 106 may comprise a satellite dish (not shown) in connection with a modem (not shown) . The satellite dish (not shown) is configured to receive analog multispectral image of the area of interest from the satellite and configured to send the analog multispectral image to a modem (not shown). The modem (not shown) is configured to convert the analog multispectral image to a digital input multispectral image.
[0021] In an embodiment of the present invention, the digital input multispectral image comprises a plurality of digital numbers
(DNs) representing pixels of the digital input multispectral image. In an exemplary embodiment of the present invention, the digital input multispectral image may comprise of plurality of digital numbers representing pixels of a plurality of bands of an electromagnetic spectrum reflected from the area of interest. In another embodiment of the present invention, the digital multispectral image comprises digital numbers in terms of numerical values of radiance or reflectance from the vegetation and natural or man-made materials and features in the area of interest. In an example, a digital multispectral image is typically composed of picture elements (pixels) and the pixels are associated with digital numbers that depict average radiance of a relatively small area within a scene. A smaller digital number indicates low average radiance from the area of interest and higher digital number is an indicator of high radiant properties of the area of interest. In an exemplary embodiment of the present invention, the

multispectral image is represented by x, y and z coordinates. The x, y coordinates are pixel coordinates and the z coordinates correspond to digital number (DN) values. In another exemplary embodiment of the present invention, the receiver unit 106 is configured to convert digital multispectral images into digital multispectral data. The digital multispectral data comprises digital numbers corresponding to the pixels of the digital image.
[0022] In an embodiment of the present invention, the atmospheric data correction unit 108 of the computation engine 104 is configured to receive the digital multispectral data sent from the receiver unit 106. Further, the atmospheric data correction unit 108 is configured to apply atmospheric correction and correct the digital multispectral image for atmospheric effects, such as atmospheric scattering and absorption. In an embodiment of the present invention, the atmospheric data correction unit 108 is configured to apply a physical principle on the digital multispectral data based on radiative transfer physics based principle to generate atmospherically corrected multispectral data. In an exemplary embodiment of the present invention, the radiative transfer physics based principle models the atmosphere above a given part of the earth and includes solar conditions
(angles of solar incidence and zenith), visibility and water vapour content, etc. In an embodiment of the present invention, the radiative transfer based physics principle assumes a non-zero surface reflectance of dark objects. In an example, the dark objects may include objects such as water, shaded areas or other dark objects within the image. In an exemplary embodiment of the present invention, the atmospheric correction is performed by using software such as 6S, MODTRAN, ENVI™-FLAASHe, ATREM, ACORN, ATCORe 2 or ATCORe 3, etc.
[0023] In an embodiment of the present invention, the vegetation index computation unit 110 of the computation engine 104 is configured to compute a normalized difference vegetation index (NDVI) from the atmospherically corrected multispectral data received from the atmospheric data correction unit 108. The normalized difference vegetation index (NDVI) is computed as below:

{NDVI = (NIR-Red)/(NIR+Red)}
Where, Red and NIR stand for spectral reflectance measurements acquired in the red (visible) and near-infrared regions, respectively of the digital multispectral data.
[0024] In an embodiment of the present invention, the normalized difference vegetation index (NDVI) is computed using an open source database management system (DBMS) software. In an exemplary embodiment of the present invention, the open source DBMS software may also include commercial software e.g. Surfer61, Grapher®, etc. In an embodiment of the present invention, the normalized difference vegetation index (NDVI) is used to estimate the vegetation abundance for the vegetated pixel. The computed NDVI represents spectral response in terms of vegetation in the area of interest. The geological materials and features may relate to heterogeneous terrain of land above which the vegetation is present. In an example, the computed NDVI may relate to the heterogeneous terrain where certain areas are brightly illuminated and certain areas are not brightly illuminated. In an exemplary embodiment of the present invention, the NDVI provides a quantitative estimate of vegetation presence. In an exemplary embodiment of the present invention, the NDVI may have values in between -1 and +1.
[0025] In an embodiment of the present invention, the scatter plot unit 114 of the computation engine 104 is configured to generate a scatter plot between the computed NDVI values and the atmospherically corrected multispectral data received from the vegetation index computation unit 110 and the atmospheric data correction unit 108 respectively. In an exemplary embodiment of the present invention, the atmospherically corrected multispectral data may be in the form of American Standard Code for Information Interchange (ASCII) format. In an embodiment of the present invention, the scatter plot unit 114 is configured to generate a scatter plot such that x axis of the scatter plot represents the NDVI values and y axis of the scatter plot represents the atmospherically corrected multispectral data in terms of digital

numbers (DNs) obtained from the atmospherically corrected multispectral data received from the vegetation index computation unit 110 and the atmospheric data correction unit 108 respectively.
[0026] In an embodiment of the present invention, the scatter plot unit 114 is configured to generate a best fit curve by application of a regression model on the scatter plot. FIG. 4 shows the generation of the best fit curve by plotting a graph between the NDVI values and the DN values. In various embodiments of the present invention, a plurality of regression models are used for statistically generating the best fit curve. In an embodiment of the present invention, the regression model is based on computation of R2 values from the scatter plot that quantifies goodness of the fit and represents a fraction between 0.0 and 1.0. In an embodiment of the present invention, the best fit curve which may be a polynomial line or a curvilinear line in scatter plot yields a flat response corresponding to vegetation-invariant response for image pixel values. In an exemplary embodiment of the present invention, the best-fit curve is in the form of a horizontal line going through mean of all Y axis values when R2 values equals 0.0. After the application of regression models, the best fit curve is generated that represents flat response such that there is no change of the NDVI with the input DN values. Further, the scatter plot unit 114 is configured to generate a mathematical function corresponding to the best fit curve
(hereinafter referred as the "best fit curve equation"). Further, linear regression equation line as shown in Fig. 4 may be represented by mathematical expression y = a + bx, where a & b are scalar values and x is the DN value of the corresponding pixel of the NDVI image. Similarly the log-linear regression line as shown in Fig. 4 may be represented by mathematical expression y = aepx and the polynomial regression line may be represented by mathematical expression y = cO + cl xl + c2 x2 ••• en xn, where n is the order of polynomial function.
[0027] In an embodiment of the present invention, the band statistics computation unit 112 of the computation engine 104 is configured to receive the atmospherically corrected multispectral data and compute target DN values from the atmospherically corrected multispectral data. The target DN values represent a

mean band DN values for each input band of the atmospherically corrected multispectral data. In an example, the mean DN values is the mean of all input DN values corresponding to a single input band, so if there are 5 bands, there will be 5 mean DN values. In an exemplary embodiment of the present invention, the target DN values are computed using a commercial or an open source relational database management system (RDBMS) or an image processing software.
[0028] In an embodiment of the present invention, the pixel computation unit 116 of the computation engine 104 is configured to receive the atmospherically corrected data, the best fit curve equation and the target DN values from the atmospheric data correction unit 108, the vegetation index computation unit 110 and the band statistics computation unit 112 respectively. In an exemplary embodiment of the present invention, the pixel computation unit 116 is configured to compute new pixel values for each band in terms of digital numbers by using the below mentioned formula:
Pixel DNnew = Pixel DNoriginai x (target DN values/ Curve DNVegetation)
where Pixel DNnew represents new pixel values in terms of digital numbers(DN);
Pixel DNoriginai represent the input pixel values in terms of Digital Numbers (DNs) received from the atmospheric data correction unit 108;
target DN values represent the mean pixel values in terms of Digital Numbers (DNs) received from band statistics computation unit 112;
Curve DNvegetation represents a mathematical expression representing best fit curve generated from the scatter plot unit 114. (Included the same in the above paragraph)
[0029] In an embodiment of the present invention, the output unit 118 is configured to receive the new DN pixel values (Pixel DNnew) from the pixel computation unit 116 and further configured

to generate a vegetation suppressed image for each band from the new DN pixel values. In an embodiment of the present invention, the new DN pixel values can be in form of ASCII format that is used to generate a vegetation suppressed image. In an example, FIG. 5 illustrates vegetation suppressed image from the new DN values.
[0030] FIG. 2 is an exemplary flowchart illustrating a method for vegetation suppression.
[0031] At step 202, multispectral image data is received. In an embodiment of the present invention, the multispectral image data represents analog multispectral image captured by a plurality of sensors in a satellite by remote sensing. The analog multispectral image captured by the satellite represents image of the area of interest and atmospheric conditions existing at the time of the image acquisition by the sensors of the satellite. The analog multispectral image captured by the satellite represents physical geography of the area of interest and light reflected from the vegetation and different natural or man-made materials and features in the area of interest. In an embodiment of the present invention, the analog multispectral image captured may be represented in 3-band Red-Green-Blue colour combination such as a standard false colour composite (FCC) image, where bands near infrared, red and green are respectively represented in Red, Green and Blue colours. In standard false colour composite images, a vegetation area on the earth appears in different shades of red depending on the types and conditions of vegetation in the vegetated area, since it has a high reflectance in the Near Infrared (NIR) band. In this case, plants reflect near infrared and green light, while absorbing red. Since the plants reflect more near infrared than green, plant-covered land appears deep red. Water absorbs all three wavelengths i.e. near infrared, red and green (partially), so it appears black.
[0032] At step 204, atmospheric correction is applied on the multispectral image data. In an embodiment of the present invention, a physical principle is applied on the digital multispectral data based on radiative transfer physics based principle to generate atmospherically corrected multispectral

data. In an exemplary embodiment of the present invention, the radiative transfer physics based principle models the atmosphere above a given part of the earth and includes solar conditions (angles of solar incidence and zenith), visibility and water vapour content, etc. In an embodiment of the present invention, the radiative transfer based physics principle assumes a non-zero surface reflectance of dark objects. In an example, the dark objects may include objects such as water, shaded areas or other dark objects within the image. In an embodiment of the present invention, the atmospheric correction is performed by using software such as 6S, MODTRAN, ENVI™-FLAASH©, ATREM, ACORN, ATCOR© 2 or ATCOR© 3, etc.
[0033] At step 206, normalized difference vegetation index (NDVI) is computed from the atmospherically corrected multispectral data. In an embodiment of the present invention, the normalized difference vegetation index (NDVI) is computed as below:
{NDVI = (NIR-Red)/(NIR+Red)}
Where, Red and NIR stand for spectral reflectance measurements acquired in the red (visible) and near-infrared regions, respectively of the digital multispectral data.
[0034] In an exemplary embodiment of the present invention, the normalized difference vegetation index (NDVI) is computed using an open source database management system (DBMS) software. Examples of the open source DBMS software may also include commercial software, e.g. Surfer61, Grapher© etc. In an embodiment of the present invention, the normalized difference vegetation index (NDVI) is used to estimate the vegetation abundance for the vegetated pixel. The computed NDVI represents spectral response in terms of vegetation present in the area of interest. The geological materials and features may relate to heterogeneous terrain of land above which the vegetation is present. In an example, the computed NDVI may relate to the heterogeneous terrain where certain areas are brightly illuminated and certain areas are not brightly illuminated. In an exemplary embodiment of the present invention, the NDVI provides a quantitative estimate of vegetation

presence. In an exemplary embodiment of the present invention, the NDVI may have values in between -1 and +1.
[0035] At step 208, a scatter plot is generated between the computed NDVI values and the atmospherically corrected multispectral data. In an embodiment of the present invention, the atmospherically corrected multispectral data may be in the form of American Standard Code for Information Interchange (ASCII) format. In an embodiment of the present invention, a scatter plot is generated such that x axis of the scatter plot represents the NDVI values and y axis of the scatter plot represents the atmospherically corrected multispectral data in terms of digital numbers (DNs) obtained from the atmospherically corrected multispectral data.
[0036] At step 210, a best fit curve function is estimated based on R-squared values. Referring to Fig. 4, in an embodiment of the present invention, the regression model is based on computation of R2 values from the scatter plot that quantifies goodness of the fit and represents a fraction between 0.0 and 1.0. In an exemplary embodiment of the present invention, the best-fit curve fit is in the form of a horizontal line going through mean of all Y axis values when R2 values equals 0.0. In an embodiment of the present invention, a plurality of regression models are used for statistically generating the best fit curve. In an embodiment of the present invention, the best fit curve which may be a polynomial line or a curvilinear line in scatter plot is one that yields a flat response corresponding to vegetation-invariant response for image pixel values. After the application of regression models, the best fit curve is generated that represents flat response such that there is no change of the NDVI with the input DN values. Further, a mathematical function is generated corresponding to the best fit curve (hereinafter referred as the "best fit curve equation").
[0037] At step 212, the target DN values are computed from the atmospherically corrected multispectral data determined at step 204. In an embodiment of the present invention, the target DN values represent a mean band DN values for each input band of the

atmospherically corrected multispectral data. In an embodiment of the present invention, the target DN values are computed using a commercial or an open source relational database management system (RDBMS) or an image processing software.
[0038] At step 214, the new pixel values are computed from the atmospherically corrected data, the best fit curve equation and the target DN values. In an embodiment of the present invention, the pixel computation unit 116 is configured to compute new pixel values in terms of digital numbers by using the below mentioned formula:
Pixel DNnew = Pixel DNoriginai x (target DN values/ Curve DNVegetation)
where Pixel DNnew represents new pixel values in terms of digital numbers(DN);
Pixel DNoriginai represent the input pixel values in terms of Digital Numbers (DNs);
target DN values represent the mean pixel values in terms of Digital Numbers (DNs);
Curve DNvegetation represents a mathematical expression representing best fit curve.
[0039] At step 216, vegetation suppressed image is generated for each band from new pixel values. In an embodiment of the present invention, the new DN pixel values can be in form of ASCII format that is used to generate a vegetation suppressed image.
[0040] FIG. 6 illustrates an exemplary computer system in which various embodiments of the present invention may be implemented. The computer system 602 comprises a processor 604 and a memory 606. The processor 604 executes program instructions and is a real processor. The computer system 602 is not intended to suggest any limitation as to scope of use or functionality of described embodiments. For example, the computer system 602 may include, but not limited to, a programmed microprocessor, a micro-

controller, a peripheral integrated circuit element, and other devices or arrangements of devices that are capable of implementing the steps that constitute the method of the present invention. In an embodiment of the present invention, the memory 60 6 may store software for implementing various embodiments of the present invention. The computer system 602 may have additional components. For example, the computer system 602 includes one or more communication channels 608, one or more input devices 610, one or more output devices 612, and storage 614. An interconnection mechanism (not shown) such as a bus, controller, or network, interconnects the components of the computer system 602. In various embodiments of the present invention, operating system software (not shown) provides an operating environment for various software executing in the computer system 602, and manages different functionalities of the components of the computer system 602.
[0041] The communication channel (s) 608 allow communication over a communication medium to various other computing entities. The communication medium provides information such as program instructions, or other data in a communication media. The communication media includes, but not limited to, wired or wireless methodologies implemented with an electrical, optical, RF, infrared, acoustic, microwave, Bluetooth or other transmission media.
[0042] The input device(s) 610 may include, but not limited to, a keyboard, mouse, pen, joystick, trackball, a voice device, a scanning device, touch screen or any another device that is capable of providing input to the computer system 602. The output device (s) 612 may include, but not limited to, a user interface on CRT or LCD, printer, speaker, CD/DVD writer, or any other device that provides output from the computer system 602.
[0043] The storage 614 may include, but not limited to, magnetic disks, magnetic tapes, CD-ROMs, CD-RWs, DVDs, flash drives or any other medium which can be used to store information and can be accessed by the computer system 602. In various embodiments of the present invention, the storage 614 contains program instructions for implementing the described embodiments.

[0044] The present invention may suitably be embodied as a computer program product for use with the computer system 602. The method described herein is typically implemented as a computer program product, comprising a set of program instructions which is executed by the computer system 602 or any other similar device. The set of program instructions may be a series of computer readable codes stored on a tangible medium, such as a computer readable storage medium (storage 614), for example, diskette, CD-ROM, ROM, flash drives or hard disk, or transmittable to the computer system 602, via a modem or other interface device, over either a tangible medium, including but not limited to optical or analogue communications channel(s) 608. The implementation of the invention as a computer program product may be in an intangible form using wireless techniques, including but not limited to microwave, infrared, Bluetooth or other transmission techniques. These instructions can be preloaded into a system or recorded on a storage medium such as a CD-ROM, or made available for downloading over a network such as the internet or a mobile telephone network. The series of computer readable instructions may embody all or part of the functionality previously described herein.
[0045] The present invention may be implemented in numerous ways including as a system, a method, or a computer program product such as a computer readable storage medium or a computer network wherein programming instructions are communicated from a remote location.
[0046] While the exemplary embodiments of the present invention are described and illustrated herein, it will be appreciated that they are merely illustrative. It will be understood by those skilled in the art that various modifications in form and detail may be made therein without departing from or offending the spirit and scope of the invention.

We Claim:

1. A system for suppression of multispectral response of
vegetation in a multispectral image, wherein the system comprises:
a memory (122) storing program instructions;
a processor (120) executing program instructions stored in the memory; and
a computation engine (104) executed by the processor (120) and configured to:
receive a digital multispectral image data comprising a plurality of digital numbers, wherein the digital numbers represent pixel values of a plurality of bands of an electromagnetic spectrum reflected from a dense vegetated area;
apply an atmospheric correction using a radiative transfer physics based principle over the digital numbers to generate an atmospherically corrected multispectral data;
compute a normalized difference vegetation index (NDVI) values from the atmospherically corrected multispectral data, wherein the NDVI values are in between -1 and +1;
generate a scatter plot between the atmospherically corrected multispectral data and the NDVI values, wherein the scatter plot is used to generate a best fit curve by using regression analysis; and
compute new pixel values based on the best fit curve and generate a vegetation suppressed image from the new pixel values.
2. The system as claimed in claim 1, wherein the system comprises a pixel computation unit (116) configured to compute the new pixel values based on original digital number (DN) values corresponding to pixel values, mean DN values and regression-based best fit curve in terms of mathematical expression.
3. The system as claimed in claim 2, wherein the pixel computation unit (116) is configured to compute normalized difference vegetation index (NDVI) based on spectral reflectance measurements acquired in the red and near-infrared regions of the digital multispectral data.

4. The system as claimed in claim 1, wherein the NDVI values
are calculated using an open source database management system
(DBMS) software.
5. The system as claimed in claim 1, wherein the system comprises scatter plot unit (114) configured to compute goodness of fit from the scatter plot for computation of the best fit curve.
6. The system as claimed in claim 1, wherein the system comprises an atmospheric data correction unit (108) configured to apply the atmospheric correction using the radiative transfer physics based principle by modeling atmosphere above a given part of earth by including solar conditions such as angles of solar incidence, zenith, visibility and water vapour content.
7. The system as claimed in claim 2, wherein the pixel computation unit (116) is configured to compute NDVI representing spectral response in terms of vegetation in the area of interest.

8. The system as claimed in claim 1, wherein the pixel computation unit (116) is configured to compute new pixel values in form of ASCII format that is used to generate a vegetation suppressed image.
9. A method for suppression of multispectral response of vegetation in a multispectral image executed by a processor comprising program instructions stored in a memory, the method comprising:
receiving a digital multispectral image data comprising a plurality of digital numbers, wherein the digital numbers represent pixel values of a plurality of bands of an electromagnetic spectrum reflected from a dense vegetated area;
applying an atmospheric correction using pre-existing radiative transfer physics based principle over the digital numbers to generate an atmospherically corrected multispectral data;
computing a normalized difference vegetation index (NDVI) from the atmospherically corrected multispectral data; wherein the NDVI values are in between -1 and +1;

generating a scatter plot between the atmospherically corrected multispectral data and the NDVI values, wherein the scatter plot is used to generate a best fit curve by using regression analysis; and
computing new pixel values based on the best fit curve and generating a vegetation suppressed image from the new pixel values.
10. The method as claimed in claim 9, wherein the method comprises computing new pixel values based on original digital number (DN) values corresponding to pixel values, mean DN values and regression-based best fit curve in terms of mathematical expression.
11. The method as claimed in claim 9, wherein the method comprises computing normalized difference vegetation index (NDVI) based on spectral reflectance measurements acquired in the red (visible) and near-infrared regions of the digital multispectral data.

12. The method as claimed in claim 9, wherein the NDVI values are calculated using an open source database management system (DBMS) software.
13. The method as claimed in claim 9, wherein the method comprises computing goodness of fit from the scatter plot for computation of the best fit curve.
14. The method as claimed in claim 9, wherein the step of applying the atmospheric correction using pre-existing radiative transfer physics based principle comprises modelling atmosphere above a given part of earth by including solar conditions such as angles of solar incidence, zenith, visibility and water vapour content.
15. The method as claimed in claim 9, wherein the method comprises computing NDVI representing spectral response in terms of vegetation in the area of interest.

16. The method as claimed in claim 9, wherein the method comprises computing new pixel values in form of ASCII format that is used to generate a vegetation suppressed image.