Conference Papers
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Item Monitoring land use and land cover changes in coastal karnataka(Springer Science and Business Media Deutschland GmbH, 2021) Kumar, M.S.; Venkatesh, V.; Gowthami, S.B.; Anjita, N.A.; Nayana, N.; Regi, L.; Dwarakish, G.S.The dynamics of land use/land cover can be studied by using digital change detection techniques which are highly significant for the evaluation and development of management strategies in a region. The environmental and hydrological processes prevailing in the area can be interpreted only by analyzing the alterations in the past and present land use and land cover classes. In view of this, the present study is executed to analyze the typical land use change in the coastal region over the three decades by analyzing historical and current land use/land cover (LU/LC) datasets. Landsat 5 and Landsat 8 satellite datasets were considered for change detection analysis using unsupervised classification method. K-means algorithm, a widely used unsupervised classification technique, was adopted in this study to classify coastal region of Karnataka for the years 1990 and 2019. The level-II classification was performed on LU/LC raster datasets (Landsat 5 and 8) which segregated the entire study area into ten classes, namely agricultural land, barren land, built-up area, water, forest, fallow or cultivated land, scrub forest, sandy area, swampy forest and wetlands. This study encapsulated that about 40% of the study area was occupied by water body followed by forestry with a percentage of around 30%. Major changes were observed in the barren land and scrub forest between 1990 and 2019, where the barren land was replaced by scrub forest in 2019. The accuracy assessment is performed to analyze the efficiency of the algorithm and the precision of the classified image which showed an accuracy of 81% in 1990 and 84% in 2019 demonstrating the ability of an algorithm to classify reliably. © Springer Nature Singapore Pte Ltd 2021.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.Item Attention-Based Bitemporal Image Deep Feature-Level Change Detection for High Resolution Imagery(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.To understand the intricacy of changes on the surface of the land, change detection is an important field in the area of remote sensing. Bitemporal remote sensing images are resourceful information to perform the analysis related to classification and change detection. Most of the architectures proposed for improving the performance of change detection in high resolution images pose a challenge due to composite texture features and finer image details. In this paper, we propose a change detection approach for bitemporal images using supervised learning. Firstly, extraction of the features is performed using a pretrained neural network. Then, the extracted features are provided to a (DSDEN) deep supervised-based difference evaluation network. Then, channel and spatial-based attention components are incorporated for fusing the difference image features with the deep features of raw images for the reconstruction of the final change map. The experimental evaluation on public “LEVIR-CD†dataset demonstrates the effectiveness and superiority over traditional methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item DETECTION OF BUILDING INFRASTRUCTURE CHANGES FROM BI-TEMPORAL REMOTE SENSING IMAGES(Institute of Electrical and Electronics Engineers Inc., 2024) Sravya, N.; Kevala, V.D.; Akshaya, P.; Basavaraju, K.S.; Lal, S.; Gupta, D.Change detection (CD) from satellite images is crucial for Earth observation, especially in monitoring urban growth patterns. Recent research has largely focused on using Deep Learning (DL) techniques, particularly variations of Convolutional Neural Network (CNN) architectures. While DL methods have shown promise, many of the models could not preserve the changed areas shape and it fails in predicting the correct edges of changed areas. This paper introduces a CNN based Building Infrastructure Change Detection Network (BICDNet) for predicting changes from bi-temporal remote sensing images. The model leverages a modified Fully Convolutional Siamese-Difference Network to extract detailed features from given images which includes a Multi-Feature Extraction (MFE) block designed to capture features from changed areas of various size within the given input images. To further refine these feature pairs, a modified Atrous Spatial Pyramid Pooling (MASPP) module is integrated, which effectively captures contextual information at multiple scales. The comparison study shows that the proposed BICDNet performs better than the existing CD models. © 2024 IEEE.