Browsing by Author "Bobate, N."

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    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.
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    Fusing Conventional and Deep Learning Features for Hyperspectral Image Change Detection
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bobate, N.; Yadav, P.P.; Narasimhadhan, A.V.
    In the field of remote sensing technology Change Detection (CD) is one of the major areas of research. Changes that have occurred on the earth's surface over time can be detected with this tool. Hyperspectral Image (HSI) data with high spectral resolution can help in identifying subtle changes than the typical multispectral image (MSI), and CD technology has benefitted immensely with the applications of HSI. Traditional CD techniques that used MSI as their input data are challenging to implement on HSI due to the high dimensionality of hyperspectral data. Furthermore, HSI data is affected by a lot of distortion and redundancy, contaminating the spectral-only information for CD purposes. CD accuracy can be improved by extracting the useful features of HSI. In Change Detection algorithms, the initial step is to extract features. Traditionally it is done using arithmetic operation, image transformation, and statistical methods. While some advanced strategies for extracting features are utilizing convolutional neural networks (CNNs) using the deep learning method. In this work, we aimed to integrate the conventional features with CNN extracted features to boost the overall ac-curacy of popular DL-based CD techniques. Spectral matching algorithms are used for extracting conventional features. In addition, appropriate changes are made to the recent deep learning architectures called Three-Directions Spectral-Spatial Convolution neural network (TDSSC) and General End-To-End Neural Network (GETNET), to fuse the conventional features. Farmland, River and USA data sets are used for experimentation. The proposed approach proves to be useful in improving the performance of DL-based CD techniques. © 2022 IEEE.