1. A smart explainable hyperspectral analysis method for band selection and interpretation, comprising: a. a data acquisition module configured to capture hyperspectral data (102) of a target object or environment; b. a preprocessing (104) module operatively coupled to the data acquisition module and configured to normalize, denies, and condition the hyperspectral data (102); c. a spectral band selection module configured to identify and select one or more critical spectral bands (106) from the hyperspectral data (102) based on predefined relevance criteria; d. an analysis module (108) configured to process the selected spectral bands to generate at least one output comprising a prediction, classification, or analytical result; e. an explain ability module configured to determine a contribution (110)) of each selected spectral band (106) to the generated output and to provide an explanation of the output; f. a physiological interpretation module configured to map spectral characteristics of the selected spectral bands to one or more real-world physiological or physical parameters; and g. wherein the output module configured to present the generated output along with corresponding explanations and physiologically or physically meaningful interpretations (112).

2. The method as claimed in claim 1, wherein the data acquisition module comprises a hyperspectral data (102) imaging sensor configured to capture spectral data across a plurality of wavelengths.

3. The method as claimed in claim 1, wherein the preprocessing (104) module is configured to perform noise reduction, spectral normalization, and correction of distortions in the hyperspectral data (102).

4. The method as claimed in claim 1, wherein the spectral band selection module employs statistical methods, feature importance ranking, or machine learning techniques to identify the critical spectral bands (106).

5. The method as claimed in claim 1, wherein the spectral band selection module is configured to reduce data dimensionality by eliminating redundant or irrelevant spectral bands.

6. The method as claimed in claim 1, wherein the analysis module (108) comprises one or more machine learning models selected from the group consisting of classification models, regression models, and deep learning models.

7. The method as claimed in claim 1, wherein the explainability module is configured to assign importance scores to each selected spectral band and generate an explanation of their contribution (110) to the output.

8. The method as claimed in claim 1, wherein the explainability module utilizes model-agnostic or model-specific explainability techniques to interpret the analysis results.

9. The method as claimed in claim 1, wherein the physiological interpretation (112) module is configured to correlate spectral characteristics with biological, chemical, or material properties.

10. The method as claimed in claim 1, wherein the physiological interpretation (112) module identifies parameters including at least one of chlorophyll content, moisture level, tissue composition, or material composition.

6. DATE AND SIGNATURE Dated this on 06th April 2026 Signature Mr. Srinivas Maddipati (IN/PA 3124) Agent for applicant

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to the field of hyperspectral data analysis and artificial intelligence. More particularly, the invention relates to a smart and explainable system and method for analyzing hyperspectral data, identifying critical spectral bands, and providing physiologically or physically meaningful interpretations.

Background of the Invention

Hyperspectral imaging has emerged as a powerful technique for capturing detailed information across a wide range of electromagnetic wavelengths. Unlike conventional imaging systems that rely on limited spectral bands, hyperspectral systems acquire rich spectral signatures for each pixel, enabling precise analysis of material composition, biological conditions, and environmental characteristics. This capability has led to widespread adoption in domains such as agriculture, medical diagnostics, industrial inspection, and remote sensing.

Despite these advantages, conventional hyperspectral analysis approaches face significant challenges. One of the primary limitations is the use of the entire spectral dataset without effectively identifying the most relevant spectral bands. This results in high data dimensionality, increased computational complexity, and redundancy, making real-time analysis difficult and resource-intensive.

Another major drawback of existing systems is their reliance on complex machine learning and deep learning models that operate as “black boxes.” While such models may achieve high accuracy, they often fail to provide insight into how decisions are made. This lack of explain ability reduces user trust and limits adoption in critical applications where interpretability and transparency are essential.

Existing hyperspectral analysis techniques typically provide outputs in the form of classifications or predictions without linking them to meaningful physiological or physical interpretations. As a result, users are unable to understand the underlying causes of the observed outcomes, such as specific biological changes or material properties associated with spectral variations.

Accordingly, there exists a need for an improved hyperspectral analysis system that can efficiently identify critical spectral bands, reduce data redundancy, provide explainable decision-making, and generate outputs that are directly interpretable in terms of real-world physiological or physical conditions.

Objects of the Invention

The principal object of the present invention is to provide a smart hyperspectral analysis system for identifying critical spectral bands from hyperspectral data.

Another object of the present invention is to provide an explainable analytical framework that determines the contribution of selected spectral bands to the generated output.

Another object of the present invention is to reduce computational complexity and data redundancy by selecting only relevant spectral bands.

Another object of the present invention is to provide physiologically or physically meaningful interpretations based on spectral characteristics.

Another object of the present invention is to enhance transparency, reliability, and user trust in hyperspectral analysis systems.

Another object of the present invention is to enable efficient and scalable hyperspectral data analysis applicable across multiple domains including agriculture, healthcare, industrial inspection, and environmental monitoring.

Another object of the present invention is to provide a system capable of real-time or near real-time hyperspectral data processing for rapid decision-making.

Another object of the present invention is to integrate spectral band selection with explain ability and interpretation in a unified framework for improved analytical performance and usability.

Brief Summary of the Invention

The present invention provides a smart explainable hyperspectral analysis system and method for band selection and interpretation. The invention is configured to process hyperspectral data efficiently by identifying critical spectral bands and generating meaningful interpretations associated with the analyzed data.

In one aspect, the system comprises a data acquisition module configured to capture hyperspectral data from a target object or environment. The acquired data is further processed by a preprocessing module to remove noise, normalize spectral values, and improve data quality for subsequent analysis.

In another aspect, the invention includes a spectral band selection module configured to identify and select critical spectral bands based on relevance criteria. This selective approach reduces data dimensionality and computational burden while retaining significant spectral information necessary for accurate analysis.

The selected spectral bands are processed by an analysis module to generate predictions, classifications, or analytical outputs. An explain ability module is further configured to determine the contribution of each selected spectral band, thereby providing transparency in the decision-making process.

A physiological interpretation module maps the spectral characteristics to real-world physiological or physical parameters, enabling meaningful understanding of the results. This allows the system to bridge the gap between spectral data and practical domain-specific insights.

The invention additionally includes an output module configured to present results along with explanations and interpretations. The proposed system thus provides an efficient, transparent, and interpretable framework for hyperspectral data analysis, making it suitable for applications across agriculture, healthcare, industrial inspection, and environmental monitoring.

Brief Description of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1 illustrates a flowchart of a method for smart explainable hyperspectral data (102) analysis, comprising acquiring hyperspectral data, preprocessing, selecting critical spectral bands, analyzing the selected bands, and providing explanation and interpretation, in accordance with an exemplary embodiment of the present invention;

Detailed Description of the Invention

It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. In addition, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

According to an exemplary embodiment of the present invention, a smart explainable hyperspectral analysis system for band selection and interpretation is provided, configured to process hyperspectral data and generate interpretable outputs associated with real-world physiological or physical parameters. The invention integrates spectral band selection, explain ability, and meaningful interpretation within a unified framework to overcome limitations of conventional systems.

In accordance with an exemplary embodiment of the present invention, the system comprises a data acquisition module configured to capture hyperspectral data of a target object, surface, or environment using a hyperspectral imaging sensor, wherein the captured data includes spectral information across a plurality of wavelengths.

In accordance with an exemplary embodiment of the present invention, the acquired hyperspectral data is processed by a pre-processing module configured to perform noise reduction, normalization, and correction of spectral distortions, thereby enhancing data quality for further analysis.

In accordance with an exemplary embodiment of the present invention, a spectral band selection module is configured to identify critical spectral bands from the pre-processed hyperspectral data using statistical methods, feature importance measures, or machine learning techniques.

In accordance with an exemplary embodiment of the present invention, the spectral band selection module reduces dimensionality of the hyperspectral data by eliminating redundant or irrelevant spectral bands, thereby improving computational efficiency and processing speed.

In accordance with an exemplary embodiment of the present invention, an analysis module is configured to process the selected spectral bands using machine learning models, statistical approaches, or hybrid techniques to generate predictions, classifications, or analytical outputs.

In accordance with an exemplary embodiment of the present invention, an explain ability module is configured to determine the contribution of each selected spectral band toward the generated output and assign importance scores, thereby providing transparency in the analytical process.

In accordance with an exemplary embodiment of the present invention, a physiological interpretation module is configured to map spectral characteristics of the selected bands to real-world physiological or physical parameters based on predefined or learned relationships.

In accordance with an exemplary embodiment of the present invention, the physiological interpretation module correlates spectral variations with domain-specific attributes including biological conditions, material composition, or environmental properties.

In accordance with an exemplary embodiment of the present invention, an output module is configured to present results including predictions, explanations of contributing spectral bands, and corresponding physiological or physical interpretations in visual, graphical, or textual formats.

In accordance with an exemplary embodiment of the present invention, the system operates by acquiring hyperspectral data, pre-processing the data, selecting critical spectral bands, analysing the selected bands, determining band contributions, and mapping the results to meaningful interpretations.

In accordance with an exemplary embodiment of the present invention, the system is applicable across multiple domains including agriculture, healthcare, industrial inspection, and environmental monitoring, thereby providing an efficient, transparent, and interpretable hyperspectral analysis framework.

In references to figures, Fig. 1 illustrates a flowchart representing a method for smart explainable hyperspectral data (102) analysis for band selection and interpretation (112), comprising acquiring hyperspectral data (102) of a target object or environment, preprocessing (104) the data to remove noise and normalize spectral values, selecting critical spectral bands (106) based on relevance criteria, analyzing (108) the selected bands to generate outputs, determining contribution (110) of each spectral band, and providing explanation and interpretation (112) by mapping spectral characteristics to meaningful physiological or physical parameters.
, Claims:CLAIMS
I/We Claim:
1. A smart explainable hyperspectral analysis method for band selection and interpretation, comprising:
a. a data acquisition module configured to capture hyperspectral data (102) of a target object or environment;
b. a preprocessing (104) module operatively coupled to the data acquisition module and configured to normalize, denies, and condition the hyperspectral data (102);
c. a spectral band selection module configured to identify and select one or more critical spectral bands (106) from the hyperspectral data (102) based on predefined relevance criteria;
d. an analysis module (108) configured to process the selected spectral bands to generate at least one output comprising a prediction, classification, or analytical result;
e. an explain ability module configured to determine a contribution (110)) of each selected spectral band (106) to the generated output and to provide an explanation of the output;
f. a physiological interpretation module configured to map spectral characteristics of the selected spectral bands to one or more real-world physiological or physical parameters; and
g. wherein the output module configured to present the generated output along with corresponding explanations and physiologically or physically meaningful interpretations (112).

2. The method as claimed in claim 1, wherein the data acquisition module comprises a hyperspectral data (102) imaging sensor configured to capture spectral data across a plurality of wavelengths.

3. The method as claimed in claim 1, wherein the preprocessing (104) module is configured to perform noise reduction, spectral normalization, and correction of distortions in the hyperspectral data (102).
4. The method as claimed in claim 1, wherein the spectral band selection module employs statistical methods, feature importance ranking, or machine learning techniques to identify the critical spectral bands (106).

5. The method as claimed in claim 1, wherein the spectral band selection module is configured to reduce data dimensionality by eliminating redundant or irrelevant spectral bands.

6. The method as claimed in claim 1, wherein the analysis module (108) comprises one or more machine learning models selected from the group consisting of classification models, regression models, and deep learning models.

7. The method as claimed in claim 1, wherein the explainability module is configured to assign importance scores to each selected spectral band and generate an explanation of their contribution (110) to the output.

8. The method as claimed in claim 1, wherein the explainability module utilizes model-agnostic or model-specific explainability techniques to interpret the analysis results.

9. The method as claimed in claim 1, wherein the physiological interpretation (112) module is configured to correlate spectral characteristics with biological, chemical, or material properties.

10. The method as claimed in claim 1, wherein the physiological interpretation (112) module identifies parameters including at least one of chlorophyll content, moisture level, tissue composition, or material composition.

6. DATE AND SIGNATURE
Dated this on 06th April 2026
Signature

Mr. Srinivas Maddipati
(IN/PA 3124)
Agent for applicant