AVIRIS Research Articles

What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery.

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods.

An efficient lossless compression technique for remote sensing images using segmentation based band reordering heuristics

ABSTRACT The size of remote sensing (RS) image is indeed massive due to hundreds capturing wavelengths bands used for collecting information about the ground surface. The data in the RS images are often redundant spatially, spectrally and temporally which thus give a sufficient prospect for compressing the images in different domains. In this paper, we propose a band reordering scheme to be applied on the segmented subgroup datasets of an original RS image dataset towards presenting a hybrid lossless compression technique. The proposed dataset segmentation-based band reordering process employs two heuristics, i.e. consecutive continuity limit of previous bands and continuity breakdown thresholds to be used for efficient coding of reordered dataset towards attaining better on-board compression. The aim of the proposed compression strategy is thus to increase compression gain through reordering each highly correlated bands’ segment in the RS data-cube for efficient coding. The performance of the proposed band reordering method is measured using the Consultative Committee for Space Data Systems (CCSDS) recommended lossless CCSDS 123.0-B-1 and three-dimensional (3D) Context-based Adaptive Lossless Image Codec (CALIC) predictors. The experimental results illustrate substantially higher compression of the proposed method than that of the state-of-the-art standards using various real RS hyperspectral and hyperspectral sound datasets. Quantitatively, the compression performances are increased by 0.10%–0.66% using CCSDS and 0.65–2.52% using 3D-CALIC for hyperspectral Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) datasets, 0.26%–1.00% using CCSDS and 2.16–2.57% using 3D-CALIC for hyperspectral Hyperion datasets and 4.61%–7.36% using CCSDS and 4.05%–4.88% using 3D-CALIC for hyperspectral sounder datasets by the proposed compression technique. Besides, the proposed compression scheme outperforms state-of-the-art investigated compression methods.

Lossy Hyperspectral Image Compression on a Reconfigurable and Fault-Tolerant FPGA-Based Adaptive Computing Platform

This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manages the losses the compressor may introduce to achieve higher compression ratios without compromising the recovered image quality. The algorithm has been implemented using High-Level Synthesis (HLS) techniques to increase design productivity by raising the abstraction level. The proposed lossy compression solution is deployed onto ARTICo3, a dynamically reconfigurable multi-accelerator architecture, obtaining a run-time adaptive solution that enables user-selectable performance (i.e., load more hardware accelerators to transparently increase throughput), power consumption, and fault tolerance (i.e., group hardware accelerators to transparently enable hardware redundancy). The whole compression solution is tested on a Xilinx Zynq UltraScale+ Field-Programmable Gate Array (FPGA)-based MPSoC using different input images, from multispectral to ultraspectral. For images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the proposed implementation renders an execution time of approximately 36 s when 8 accelerators are compressing concurrently at 100 MHz, which in turn uses around 20% of the LUTs and 17% of the dedicated memory blocks available in the target device. In this scenario, a speedup of 15.6× is obtained in comparison with a pure software version of the algorithm running in an ARM Cortex-A53 processor.

Hyperspectral remote sensing image classification based on tighter random projection with minimal intra-class variance algorithm

Aiming at solving the problem of image size limiting in the traditional Random Projection (RP) algorithm, a novel Tighter Random Projection (TRP), which combines the scheme with Minimal Intra-class Variance (TRP-MIV) for hyperspectral remote sensing image classification is proposed. First, a new tighter dimensional boundary for expanding image size with the TRP-MIV matrix selected by multiple sampling for improving the class separability is defined to reduce dimension. Then the proposed algorithm is implemented, which integrates TRP-MIV for dimensionality reduction and Minimum Distance (MD) classifier for image classification. Finally, the image size and dimensionality reduction are evaluated by the number of spectral pixels under different theorems, and the spectral difference before and after dimensionality reduction, respectively. Classification performance is evaluated by kappa coefficient, Overall Accuracy (OA), Average Accuracy (AA), Average Precision Rate (APR) and running time. Classification results are obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scanner and the Reflective Optics System Imaging Spectrometer (ROSIS) scanner, which indicate that the proposed algorithm is efficient and promising.

Quantitative Analysis of Mixed Pixels in Hyperspectral Image Using Fractal Dimension Technique

In this paper, a novel approach for identification of the components of a mixed pixel along with the quantitative analysis of the determined components of hyperspectral data has been proposed. The overall method is divided into three phases. First, the spectral sensitivity curves (SSCs) of the identified endmembers soil, water and vegetation using N-FINDR algorithm are generated. Next, the percentage of the mixture of the endmembers is computed for calculating the fractal dimension (FD). Afterward, the FD of SSCs is utilized for determining the mathematical model of the obtained curve. In order to verify that the adjacent pixels belong to the class identified using N-FINDR algorithm, two algorithms, i.e., spectral angle mapper and spectral information divergence, are employed for determining the spectral similarity of the mixed pixel with the reference spectrum. Lastly, the quantitative analysis of the SSC of the mixed pixel is performed by using the corresponding calculated FD in the derived mathematical model. The performance of the mathematical model is evaluated on the airborne visible infrared imaging spectrometer image data, and the outcome shows that the proposed framework achieves very promising results with a small number of mixed pixel samples.

Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have potential for emissions reduction. Methane point source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large datasets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched filter retrieval of trace gas concentration-pathlength. The new algorithm was tested using AVIRIS-NG data acquired over several point source plumes in Ahmedabad, India. The algorithm was validated using simulated AVIRIS-NG data including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root mean squared error of retrieved methane concentration-pathlength enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flightline 2016 AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU acceleration.

Lossless Compression for Hyperspectral Images based on Adaptive Band Selection and Adaptive Predictor Selection

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

RANDOM PROJECTION BASED BIAS-CORRECTED FUZZY C-MEANS ALGORITHM FOR HYPERSPECTRAL REMOTE SENSING IMAGE SEGMENTATION

Abstract. To address the issue of the information redundancy for hyperspectral remote sensing image, this paper presents a novel ensemble algorithm that merges Random Projection (RP) and Bias-corrected Fuzzy C-means (BCFCM) algorithm. Since RP matrix has the abilities of preserving information nicely, it can be used to reduce the dimension of the image. To make full advantage of neighborhood relationship, BCFCM algorithm is improved to segment the low-dimensional image, in which Euclidean distances are retained to define the similarity between hyperspectral remote sensing image and the low-dimensional image. Finally, BCFCM algorithm is used to segment the fuzzy membership matrix of the ensemble algorithm. The proposed algorithm is evaluated by real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral remote sensing images. Segmentation performance is estimated by kappa coefficient and overall accuracy. Experimental results demonstrate that the proposed algorithm can achieve higher segmentation accuracy at a lower computational cost than that from conventional algorithms.

A new fusion of mutual information and Otsu multilevel thresholding technique for hyperspectral band selection

Hyperspectral data are a curse with huge dimensionality, high redundancy of spectral information, and are noisy in nature. Hundreds of narrow adjacent bands are present in HS (Hyperspectral) data with high spectral information and it always leads to a computational complexity in space and time. The information theoretic methods are used for hyperspectral band selection to avoid computational complexity. In order to address this issue, the new fusion of Mutual Information (MI) with Otsu (MI_Otsu) threshold method is proposed for hyperspectral band selection by employing three different entropy measures such as joint, conditional, and relative. The proposed approach identifies the probabilities, entropy, and mutual information between two hyperspectral bands. The optimal threshold is obtained using Otsu multi-threshold technique and highly informative bands will be selected. In addition, the SVM (Support Vector Machine) classification technique is adapted for further classification of selected bands to analyze the performance of the proposed algorithm. The experimental analysis is carried out using the real-time dataset from the test site ‘Indian Pines’ in Northwestern Indiana recorded by AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor that demonstrates the effectiveness of this proposed approach. It is proved that the proposed work shows the competitive performance even with less selected bands and the relative MI_Otsu method shows a higher accuracy of 92.16% with the comparison of joint and conditional MI_Otsu.

Inferring Species Diversity and Variability over Climatic Gradient with Spectral Diversity Metrics

Filling in the void between forest ecology and remote sensing through monitoring biodiversity variables is of great interest. In this study, we utilized imaging spectroscopy data from the ISRO–NASA Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) India campaign to investigate how the measurements of biodiversity attributes of forests over wide areas can be augmented by synchronous field- and spectral-metrics. Three sites, Shoolpaneshwar Wildlife Sanctuary (SWS), Vansda National Park (VNP), and Mudumalai Tiger Reserve (MTR), spread over a climatic gradient (rainfall and temperature), were selected for this study. Abundant species maps of three sites were produced using a support vector machine (SVM) classifier with a 76–80% overall accuracy. These maps are a valuable input for forest resource management. Convex hull volume (CHV) is computed from the first three principal components of AVIRIS-NG spectra and used as a spectral diversity metric. It was observed that CHV increased with species numbers showing a positive correlation between species and spectral diversity. Additionally, it was observed that the abundant species show higher spectral diversity over species with lesser spread, provisionally revealing their functional diversity. This could be one of the many reasons for their expansive reach through adaptation to local conditions. Higher rainfall at MTR was shown to have a positive impact on species and spectral diversity as compared to SWS and VNP. Redundancy analysis explained 13–24% of the variance in abundant species distribution because of climatic gradient. Trends in spectral CHVs observed across the three sites of this study indicate that species assemblages may have strong local controls, and the patterns of co-occurrence are largely aligned along climatic gradient. Observed changes in species distribution and diversity metrics over climatic gradient can help in assessing these forests’ responses to the projected dynamics of rainfall and temperature in the future.

Geometrical constrained independent component analysis for hyperspectral unmixing

ABSTRACT One of the limitations of the hyperspectral remote sensing application is the existence of mixed pixels in image data. Spectral decomposition, which separates the mixed pixels into a set of endmember spectra and abundance fractions, is the most effective way to solve the mixed pixel problem. Due to the independence of source signals, independent component analysis (ICA) has been developed for hyperspectral unmixing by adding auxiliary constraints. Abundance sum-to-one and nonnegative constraints are two obvious features for hyperspectral data. In this paper, by processing these two constraints sequentially from the geometrical point of view to restrain the sum-to-one constraint thoroughly at each iteration, geometrical constrained ICA (GCICA) is proposed based on treating the abundance distribution as the independent signal. To validate the proposed algorithm, the synthetic data, and real image data are used for unmixing, respectively. Synthetic data are generated based on spectra from the ENVI (Environment for Visualizing Images) software spectral library. The real images are used with three hyperspectral datasets, AVIRIS (Airborne Visible Infrared Imaging Spectrometer) Cuprite dataset, AVIRIS Indiana Pine dataset and HYDICE (Hyperspectral Digital Imagery Collection Experiment) dataset. Results, in comparison with previously proposed algorithms, show that the proposed method has better performance for the decomposition of hyperspectral data in abundance and endmember spectral extraction, thus providing a new and effective method for spectral unmixing and signal separation without prior information.

Class-Wise Distribution Adaptation for Unsupervised Classification of Hyperspectral Remote Sensing Images

Class-wise adversarial adaptation networks are investigated for the classification of hyperspectral remote sensing images in this article. By adversarial learning between the feature extractor and the multiple domain discriminators, domain-invariant features are generated. Moreover, a probability-prediction-based maximum mean discrepancy (MMD) method is introduced to the adversarial adaptation network to achieve a superior feature-alignment performance. The class-wise adversarial adaptation in conjunction with the class-wise probability MMD is denoted as the class-wise distribution adaptation (CDA) network. The proposed CDA does not require labeled information in the target domain and can achieve an unsupervised classification of the target image. The experimental results using the Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data demonstrated its efficiency.

Quantifying uncertainty for remote spectroscopy of surface composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data

The next generation of spaceborne imaging spectrometers will enable hyperspectral analysis of vegetation cover across large spatial extents. Spectral unmixing provides a means to assess subpixel vegetation composition in such imagery. Here we implement a regression-based unmixing approach to generate fractional vegetation cover on a regional scale from a simulated Environmental Mapping and Analysis Program (EnMAP) satellite scene derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over the San Francisco Bay Area, California, USA, an area with a mixture of temperate and Mediterranean climate forests, woodlands and shrublands. A hierarchical classification scheme was implemented that considered fractional cover of vegetation as a whole (vegetation vs non-vegetation), vegetation life forms (woody vs non-woody vegetation; tree vs shrub vs grass), and tree leaf type (needleleaf vs broadleaf). A Gaussian Process Regression (GPR) model was trained using synthetically-mixed training data generated from an endmember library, and mapping accuracy was assessed using an independent validation dataset across four ecoregions. Our approach was able to effectively model landscape patterns at all levels of the class hierarchy. Site-wide map accuracy was highest when mapping generic vegetation fractions (MAE = 3.8%) and expectedly decreased at more complex hierarchy levels, with highest errors observed when separating tree and shrub fractions. Still, fraction estimates of needleleaf trees (MAE = 10.6%), broadleaf trees (MAE = 13.1%) and shrubs (MAE = 15.3%) were mapped with low overall error. Using Landsat imagery led to an average decrease in map accuracy of 1.9% when compared to hyperspectral image analysis and a maximum decrease of 3.5% when separating broadleaf and needleleaf trees across all sites. Further, a single regional model was shown to yield comparable results to multiple local ecoregion-based models, facilitating the analysis of large regions without creating a separate model for each region. Our results highlight the utility of regression-based approaches for quantitative vegetation mapping, which is of particular interest for future spaceborne imaging spectroscopy missions operating across large areas at moderate spatial resolution.

Classification of hyperspectral images based on two-channel convolutional neural network combined with support vector machine algorithm

Our study uses artificial intelligence and machine learning methods for dimensionality reduction, feature extraction, and classification of hyperspectral remote sensing images to detect subtle differences between different spectra. Our study also used hyperspectral remote sensing image data gathered by airborne visible infrared imaging spectrometer over the Indian Pines and Salinas test sites. Based on experimental analysis and comparison of mainstream classifiers and advanced algorithms, we propose a new classification mode for the hyperspectral image based on a two-channel fusion of convolutional neural network-support vector machine (CNN-SVM). This model gives full play to the advantages of CNN in feature extraction and the advantages of SVM in the classification. It also maximizes the generalization and accuracy of classification. The CNN is based on two-channel feature extraction, including spatial features and spectral features, which utilizes the characteristics of hyperspectral remote sensing images for classification. Experimental results show that the research is both theoretically and practically significant for improving the quality and accuracy of identification and classification.

Classification Endmember Selection with Multi-Temporal Hyperspectral Data

In hyperspectral image classification, so-called spectral endmembers are used as reference data. These endmembers are either extracted from an image or taken from another source. Research has shown that endmembers extracted from an image usually perform best when classifying a single image. However, it is unclear if this also holds when classifying multi-temporal hyperspectral datasets. In this paper, we use spectral angle mapper, which is a frequently used classifier for hyperspectral datasets to classify multi-temporal airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral imagery. Three classifications are done on each of the images with endmembers being extracted from the corresponding image, and three more classifications are done on the three images while using averaged endmembers. We apply image-to-image registration and change detection to analyze the consistency of the classification results. We show that the consistency of classification accuracy using the averaged endmembers (around 65%) outperforms the classification results generated using endmembers that are extracted from each image separately (around 40%). We conclude that, for multi-temporal datasets, it is better to have an endmember collection that is not directly from the image, but is processed to a representative average.

Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States

Invasive plants are causing massive economic and environmental troubles for our societies worldwide. The aim of this study is to employ a set of machine learning classifiers for detecting invasive plant species using remote sensing data. The target species is Kudzu vine, which mostly grows in the south-eastern states of the US and quickly outcompetes other plants, making it a relevant and threatening species to consider. Our study area is Atlanta, Georgia and the surrounding area. Five different algorithms: Boosted Logistic Regression (BLR), Naive Bayes (NB), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM) were tested with the aim of testing their performance and identifying the most optimal one. Furthermore, the influence of temporal, spectral and spatial resolution in detecting Kudzu was also tested and reviewed. Our finding shows that random forest, neural network and support vector machine classifiers outperformed. While the achieved internal accuracies were about 97%, an external validation conducted over an expanded area of interest resulted in 79.5% accuracy. Furthermore, the study indicates that high accuracy classification can be achieved using multispectral Sentinel-2 imagery and can be improved while integrating with airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral data. Finally, this study indicates that dimensionality reduction methods such as principal component analysis (PCA) should be applied cautiously to the hyperspectral AVIRIS data to preserve its utility. The applied approach and the utilized set of methods can be of interest for detecting other kinds of invasive species as part of fulfilling UN sustainable development goals, particularly number 12: responsible consumption and production, 13: climate action, and 15: life on land.

Hyperspectral image remote sensing classification using RotBoost

Hyperspectral images are able to provide more information because this kind of images have hundreds of spectral bands. In machine learning, the classification of hyperspectral data has several challenges, including high number of dimensions, number of output classes, and limited data references. The solution given to overcome the challenge is to use Ensemble Learning. The benefit of using Ensemble Learning is that we can improve the classification performance of hyperspectral data. One of the Ensemble Learning methods is RotBoost, which is a combination of the Rotation Forest and Adaboost methods. To find out the performance of the RotBoost method, this research used hyperspectral data of vegetation area in Indian pines, Indiana, USA that is provided by NASA’s airborne visible infra-red imaging spectrometer (AVIRIS). The RotBoost method then compared with Rotation Forest method to find a better performance. Confusion matrix used to evaluate the accuracy of each method in the classification. The performance measured is overall accuracy obtained by doing 5-fold cross validation. This experiment also conducted to find the most optimal S (number of base classifiers) and T (number of iteration in Adaboost) values. Experimental results showed that RotBoost produces better accuracy than the Rotation Forest. S and T parameter values are also not very influential on RotBoost accuracy.

Potential Use of Airborne Hyperspectral AVIRIS-NG Data for Mapping Proterozoic Metasediments in Banswara, India

Abstract Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) data with high spectral and spatial resolutions are used for mapping metasediments in parts of Banswara district, Rajasthan, India. The AVIRIS–NG image spectra of major metasedimentary rocks were compared with their respective laboratory spectra to identify few diagnostic spectral features or absorption features of the rocks. These spectral features were translated from laboratory to image and consistently present in the image spectra of these rocks across the area. After ensuring the persistency of absorption features from sample to image pixels, three AVIRIS – NG based spectral indices is proposed to delineate calcareous (dolomite), siliceous (quartzite) and argillaceous (phyllite) metasedimentary rocks. The index image composite was compared with the reference lithological map of Geological Survey of India and also was validated in the field. The study demonstrates the efficiency of AVIRIS – NG data for mapping metasedimentary units from the Aravalli Supergroup that are known to host strata bound mineral deposits.