Conference Papers
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Item Effectiveness of Phase Correlation Spectral Similarity Measure in Distinguishing Target Signatures for Hyperspectral Data Analysis(Institute of Electrical and Electronics Engineers Inc., 2020) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Hyperspectral imaging is one of the most information-rich sources of remote sensing data that exists. It can capture the entire, continuous spectrum of color and light. Feature extraction techniques that are selected for identifying diagnostic features influence classification accuracy. Spectral matching algorithms like similarity measures are developed to compare spectral features of materials with their reference spec-tral signatures in identifying different earth surfaces. Similarity measures are used as simple feature extraction techniques in target identification using hyperspectral data. Though there are several similarity measures, selecting a robust similarity measure requires further investigation. Influence of similarity measures are not studied much in distinguishing spectrally distinct signatures. In this article, we propose to study the performance of similarity measures in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering signatures of different classes and (iv) endmember extraction. Experimental results show an effective and a robust performance of proposed phase correlation similarity measure among all other similarity measures compared for all the problems under investigation. © 2020 IEEE.Item Gradient Based Spectral Similarity Measure for Hyperspectral Image Analysis(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Spectral matching algorithms or similarity measures dis-criminate spectral signatures of unknown materials by comparing them with the reference spectral signatures. Spectral matching algorithms play an important role in identifying different earth surface features using hyperspectral image analysis. Identification of diagnostic features and discriminating spectral signatures, especially of mineral signatures, which exhibit subtle differences among themselves is still a challenging task. Thus, developing or coming up with a new spectral matching algorithms is expected. Therefore, in this paper, we present a gradient based spectral similarity measure (GSSM) that captures the diagnostic (absorption) features to measure the degree of closeness between spectral signatures. Effectiveness of the proposed GSSM in distinguishing spectrally distinct signatures is studied with that of other spectral matching algorithms in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. Experimental results on a spectral library of 5 minerals formed using a benchmark mineral dataset called Cuprite data clearly show that the proposed GSSM is capable of (i) discriminating different spectral signatures in a better manner and also (ii) bringing into notice of the user the locations of diagnostic features by highlighting them to recognize easily. © 2021 IEEE.Item Gradient Correlation Incorporated Similarity Measures in Matching Spectral Signatures(Institute of Electrical and Electronics Engineers Inc., 2022) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Hyperspectral images provide ample information which is needed to be analyzed carefully by the spectral processing algorithms for identifying objects, finding minerals etc. Spectral matching algorithms (SMAs) which make use of similarity measures discriminate and identify earth surface features by comparing spectral signatures with the ground-truth. SMAs that discriminate overall patterns capturing the diagnostic features of spectral signatures is of great use. In view of this, in this paper, we explore spectral gradient as a diagnostic feature to discriminate spectral signatures. Applicability of the proposed spectral gradient which is incorporated with SMAs in distinguishing spectrally distinct signatures is experimented in the following cases: (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. Overall, the experimental results on a benchmark mineral Cuprite dataset library of five minerals have shown significantly improved performance in discriminating various spectral signatures. © 2022 IEEE.Item ATGP based Change Detection in Hyperspectral Images(IEEE Computer Society, 2022) Yadav, P.P.; Bobate, N.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Hyperspectral images (HSIs) due to advancements in spatial-spectral resolutions and availability of multi-temporal information is in demand for many remote sensing (RS) applications including change detection (CD). The high dimensionality of HSIs and limited availability of HSI-CD data sets with ground-truth change maps make CD not so easy but a difficult task. Though there are many classical and deep learning (DL) based algorithms to detect changes, the performance of classical algorithms is not up to the satisfactory level and the final performance of DL models depend on efficiency of pre-detection techniques which provide prior knowledge on changed and unchanged areas that are required to get appropriate training samples to learn to detect changes. Classical and DL approaches consider change information at pixel level i.e. pixel to pixel change either by comparing the corresponding pixels alone or with their local neighborhood pixels. Therefore, identification of features for every pixel that relate the most significant information of the whole HSI-CD data in a simple and an efficient way to detect changes effectively is the need of the hour. In addition, there is not much comprehensive study on developing CD algorithms that not only simple to use but also as efficient as that of DL models is available. Therefore, in this paper, an endmember based feature extraction is proposed to detect changes in HSI. An automatic target generation process (ATGP) algorithm is adapted to extract endmembers present in the HSI-CD data set. Then, various spectral matching algorithms are used to measure endmember relations for all the pixels so that dimensionality of the data is reduced as well as the effective features to detect changes can be extracted. The experimental results on three benchmark HSI-CD data sets show that proposed ATGP based change vector analysis (CVA) algorithm yields remarkable results on comparing both with the classical as well as DL based CD approaches. © 2022 IEEE.