Faculty Publications
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Item UCDNet: A Deep Learning Model for Urban Change Detection From Bi-Temporal Multispectral Sentinel-2 Satellite Images(Institute of Electrical and Electronics Engineers Inc., 2022) Basavaraju, K.S.; Sravya, N.; Lal, S.; Nalini, J.; Chintala, C.S.; Dell’Acqua, F.Change detection (CD) from satellite images has become an inevitable process in earth observation. Methods for detecting changes in multi-temporal satellite images are very useful tools when characterization and monitoring of urban growth patterns is concerned. Increasing worldwide availability of multispectral images with a high revisit frequency opened up more possibilities in the study of urban CD. Even though there exists several deep learning methods for CD, most of these available methods fail to predict the edges and preserve the shape of the changed area from multispectral images. This article introduces a deep learning model called urban CD network (UCDNet) for urban CD from bi-temporal multispectral Sentinel-2 satellite images. The model is based on an encoder-decoder architecture which uses modified residual connections and the new spatial pyramid pooling (NSPP) block, giving better predictions while preserving the shape of changed areas. The modified residual connections help locate the changes correctly, and the NSPP block can extract multiscale features and will give awareness about global context. UCDNet uses a proposed loss function which is a combination of weighted class categorical cross-entropy (WCCE) and modified Kappa loss. The Onera Satellite Change Detection (OSCD) dataset is used to train, evaluate, and compare the proposed model with the benchmark models. UCDNet gives better results from the reference models used here for comparison. It gives an accuracy of 99.3%, an $F1$ score ( $F1$ ) of 89.21%, a Kappa coefficient (Ka) of 88.85%, and a Jaccard index (JI) of 80.53% on the OSCD dataset. © 1980-2012 IEEE.Item DIResUNet: Architecture for multiclass semantic segmentation of high resolution remote sensing imagery data(Springer, 2022) Priyanka; Sravya, N.; Lal, S.; Nalini, J.; Chintala, C.S.; Dell’Acqua, F.Scene understanding is an important task in information extraction from high-resolution aerial images, an operation which is often involved in remote sensing applications. Recently, semantic segmentation using deep learning has become an important method to achieve state-of-the-art performance in pixel-level classification of objects. This latter is still a challenging task due to large pixel variance within classes possibly coupled with small pixel variance between classes. This paper proposes an artificial-intelligence (AI)-based approach to this problem, by designing the DIResUNet deep learning model. The model is built by integrating the inception module, a modified residual block, and a dense global spatial pyramid pooling (DGSPP) module, in combination with the well-known U-Net scheme. The modified residual blocks and the inception module extract multi-level features, whereas DGSPP extracts contextual intelligence. In this way, both local and global information about the scene are extracted in parallel using dedicated processing structures, resulting in a more effective overall approach. The performance of the proposed DIResUNet model is evaluated on the Landcover and WHDLD high resolution remote sensing (HRRS) datasets. We compared DIResUNet performance with recent benchmark models such as U-Net, UNet++, Attention UNet, FPN, UNet+SPP, and DGRNet to prove the effectiveness of our proposed model. Results show that the proposed DIResUNet model outperforms benchmark models on two HRRS datasets. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item RSCDNet: A Robust Deep Learning Architecture for Change Detection From Bi-Temporal High Resolution Remote Sensing Images(Institute of Electrical and Electronics Engineers Inc., 2023) Deepanshi; Barkur, R.; Suresh, D.; Lal, S.; Chintala, C.S.; Diwakar, P.G.Accurate change detection from high-resolution satellite and aerial images is of great significance in remote sensing for precise comprehension of Land cover (LC) variations. The current methods compromise with the spatial context; hence, they fail to detect and delineate small change areas and are unable to capture the difference between features of the bi-temporal images. This paper proposes Remote Sensing Change Detection Network (RSCDNet) - a robust end-to-end deep learning architecture for pixel-wise change detection from bi-temporal high-resolution remote-sensing (HRRS) images. The proposed RSCDNet model is based on an encoder-decoder framework integrated with the Modified Self-Attention (MSA) andthe Gated Linear Atrous Spatial Pyramid Pooling (GL-ASPP) blocks; both efficient mechanisms to regulate the field-of-view while finding the most suitable trade-off between accurate localization and context assimilation. The paper documents the design and development of the proposed RSCDNet model and compares its qualitative and quantitative results with state-of-the-art HRRS change detection architectures. The above mentioned novelties in the proposed architecture resulted in an F1-score of 98%, 98%, 88%, and 75% on the four publicly available HRRS datasets namely, Staza-Tisadob, Onera, CD-LEVIR, and WHU. In addition to the improvement in the performance metrics, the strategic connections in the proposed GL-ASPP and MSA units significantly reduce the prediction time per image (PTPI) and provide robustness against perturbations. Experimental results yield that the proposed RSCDNet model outperforms the most recent change detection benchmark models on all four HRRS datasets. © 2017 IEEE.Item Assessing forest health using remote sensing-based indicators and fuzzy analytic hierarchy process in Valmiki Tiger Reserve, India(Institute for Ionics, 2023) Roshani; Sajjad, H.; Rahaman, M.H.; Rehman, S.; Masroor, M.; Ahmed, R.Anthropogenic activities, climate variability and environmental stresses have greatly affected forest ecosystems globally. Thus, monitoring of forest health is essential for proper planning and effective management. The present study employed an integrated approach of remote sensing and fuzzy analytic hierarchy process to assess the forest health in the Valmiki Tiger Reserve in India. Advanced vegetation index, normalized difference vegetation index, normalized difference moisture index, forest fragmentation, rainfall and soil types were derived from remote sensing data. Multiple buffer zones of villages, roads, railways and canals were also determined for analyzing the forest health status. These layers were prepared in the geographical information system. These layers were given weightage using fuzzy analytic hierarchy process. These layers were integrated to prepare forest health map using weighted overlay method. The results revealed that the largest forest area was found under moderately healthy forest (37%) followed by healthy forest (31%) and unhealthy forest (13%). Of the total area of the Reserve, 19% area was under non-forest category. Human-induced disturbances such as encroachment, illegal sand mining, livestock grazing and forest conversion to agriculture have been attributed to the unhealthy forest in the Reserve. The receiver operating characteristic curve value and area under curve (0.792) show reliability of forest health map. The findings of this study may be helpful for forest managers, conservationists and local communities in devising sustainable strategies for effective management of the forest. The methodological framework adopted in this study may be utilized in other geographical regions interested in assessing forest health. © 2022, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.Item BCDetNet: a deep learning architecture for building change detection from bi-temporal high resolution satellite images(Springer Science and Business Media Deutschland GmbH, 2023) Basavaraju, K.S.; Hiren, N.S.; Sravya, N.; Lal, S.; Nalini, J.; Chintala, C.S.Change detection is becoming more and more popular technology for the analysis of remote sensing data and is very important for an accurate understanding of changes that are happening in the Earth’s surface. Different Deep Learning methods proposed till now are mainly focused on simple networks which results in poor detection for small changed areas because they can not differentiate between the bi-temporal image’s characteristics. To solve this problem, this article proposes a novel Building Change Detection Network (BCDetNet) for building object change detection and its analysis from bi-temporal high resolution satellite image. The proposed BCDetNet model can detect small change areas with the help of multiple feature extraction block. The proposed BCDetNet model executes building change detection using bi-temporal high resolution satellite images. The proposed BCDetNet model is trained on two publicly available datasets namely LEVIR and WHU change detection(CD) datasets. These datasets contain RGB images with dimensions of (1024 × 1024) and (512 × 512), respectively. The BCDetNet model can learn from scratch during training and performs better than the benchmark change detection models with fewer trainable parameters. The BCDetNet model gives Recall—94.06%, Precision—93.00%, Jaccard score—88.40%, Accuracy—98.73%, F1 score—93.52% and Kappa coefficient—87.05% on LEVIR CD dataset and Recall—89.51%, Precision —92.78%, Jaccard score - 84.38%, Accuracy—96.78%, F1 score—91.06% and Kappa coefficient - 82.12% on WHU CD dataset. This work is a step in the direction of achieving best results in building change detection from high resolution satellite images. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Canopy centre-based fuzzy-C-means clustering for enhancement of soil fertility prediction(Inderscience Publishers, 2024) Sujatha, M.; Jaidhar, C.D.For plants to develop, fertile soil is necessary. Estimating soil parameters based on time change is crucial for enhancing soil fertility. Sentinel-2’s remote sensing technology produces images that can be used to gauge soil parameters. In this study, values for soil parameters such as electrical conductivity, pH, organic carbon, and nitrogen are derived using Sentinel-2 data. In order to increase the clustering accuracy, this study suggests using Canopy centre-based fuzzy-C-means clustering and comparing it to manual labelling and other clustering techniques such as Canopy, density-based, expectation-maximisation, farthest-first, k-means, and fuzzy-C-means clustering, its usefulness is demonstrated. The proposed clustering achieved the highest clustering accuracy of 78.42%. Machine learning-based classifiers were applied to classify soil fertility, including Naive Bayes, support vector machine, decision trees, and random forest (RF). Dataset labelled with the proposed RF clustering classifier achieves a high classification accuracy of 99.69% with ten-fold cross-validation. © 2024 Inderscience Enterprises Ltd.. All rights reserved.Item Forecasting Land-Use and Land-Cover Change Using Hybrid CNN-LSTM Model(Institute of Electrical and Electronics Engineers Inc., 2024) Varma, B.; Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Rajan, J.Land-use and land-cover (LULC) information helps analyze future trends and is essential for environmental management and sustainable planning. Time-series satellite images are employed in this study to forecast changes in LULC. Deep-learning (DL) frameworks have been widely used for modeling dynamic LULC changes at the regional level. However, improving the accuracy of the existing prediction models is necessary. This letter proposes an integrated convolutional neural network (CNN) and long short-term memory network (LSTM) known as a hybrid CNN-LSTM model to address the fine-scale LULC prediction requirement. The efficiency of the proposed approach was examined using LULC data for the Dakshina Kannada District of Karnataka State, India. The proposed model achieved an overall accuracy of 95.11% and a kappa coefficient of 0.92, based on the ground-truth data for 2014. The model's predictions for 2035, based on data from 2005 to 2014, revealed the following trends: Urbanization exhibited a pattern of rapid expansion and increased growth. The integrated CNN-LSTM model extracted spatial and temporal features for effectively predicting LULC changes. Infrastructure development, population density, and enhanced economic activities were the major driving factors of changes in LULC for the study region. Robust LULC change forecasting will strengthen LULC evaluations, aid in understanding complex land-use systems, and empower decision-makers to formulate effective land management strategies in the coming years. © 2004-2012 IEEE.Item A Dual Phase Approach for Addressing Class Imbalance in Land-Use and Land-Cover Mapping From Remotely Sensed Images(Institute of Electrical and Electronics Engineers Inc., 2024) Putty, A.; Annappa, B.; Prajwal, R.; Pariserum Perumal, S.P.Semantic segmentation of remotely sensed images for land-use and land-cover classes plays a significant role in various ecosystem management applications. State-of-the-art results in assigning land-use and land-cover classes are primarily achieved using fully convolutional encoder-decoder architectures. However, the uneven distribution of the land-use and land-cover classes becomes a major hurdle leading to performance skewness towards majority classes over minority classes. This paper proposes a novel dual-phase training, with the first phase proposing a new undersampling technique using minority class focused class normalization and the second phase that uses this learnt knowledge for ensembling to prevent overfitting and compensate for the loss of information due to undersampling. The proposed method achieved an overall performance gain of up to 2% in MIoU, Kappa, and F1 Score metrics and up to 3% in class-wise F1-score when compared to the baseline models on Wuhan Dense Labeling, Vaihingen and Potsdam datasets. © 2013 IEEE.Item SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images(Institute of Electrical and Electronics Engineers Inc., 2024) Basavaraju, K.S.; Sravya, N.; Kevala, V.D.; Suresh, S.; Lal, S.Semantic change detection in remote sensing imagery plays a pivotal role in urban planning, environmental monitoring, and disaster assessment applications. Existing deep learning-based methods, particularly those relying on triple-branch architectures, often struggle to accurately localize and predict changes in complex spatial environments characterized by diverse land-cover types. To overcome these limitations, this paper proposes a novel network called the Spatial Flow-based Semantic Change Detection Network. This network processes bi-temporal satellite images using a dual-encoder, triple-decoder architecture that progressively refines spatial features at each network stage, improving semantic change detection results. The Attention-Based Siamese Encoder, Cascaded Convolutional Attention Fusion Block, Cascaded Convolutional Attention Refinement Block and Differentiable Binarization layer helps in improving semantic change detection performance. Experimental results of proposed network on the SECOND dataset demonstrate that the proposed model significantly improves the ability to localize critical changes and distinguish between change and no-change regions. The proposed network achieves an overall accuracy of 86.32%, a mean Intersection over Union of 70.33%, a Separated Kappa of 21.21%, and an F1-score for semantic change detection of 66.01%, with a score of 35.94%. These results represent substantial improvements over previous state-of-the-art models, including a 0.26% increase in overall accuracy, a 2.21% increase in mean Intersection over Union, a 2.62% enhancement in Separated Kappa, and a 3.6% improvement in F1-score for semantic change detection compared to the best-performing models. Notably, the proposed network achieves these results with only 14.56 million parameters, making it more effective and efficient than its competitors, which utilize over 22 million parameters. © 2013 IEEE.Item Novel assessment tools for inland aquaculture in the western Godavari delta region of Andhra Pradesh(Springer, 2024) Thotakura, T.V.; Malegole, S.B.; Chaudhary, B.; Gobinath, G.; Chitturi, P.; Durga Prasad, D.P.The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring. Unfortunately, technical innovation is less in aquaculture than agricultural and manufacturing industries. The advent of remote sensing and soft computing has expanded various opportunities for utilizing and integrating technological advances in civil and environmental disciplines. This paper presents the aquaculture scenario in the western Godavari delta region of Andhra Pradesh and proposes various novel assessment tools to monitor the aquaculture environment. An experimental investigation was carried out on the physicochemical characteristics of the inland aquaculture ponds to evaluate water quality in the aquaculture ponds. Furthermore, to assess the intensity of inland aquaculture, the current work concentrates on the potential application of remote sensing and soft computing approaches. Geospatial models of kriging and inverse distance weighing (IDW) show higher performance in estimating ammonia levels in the intensive aquaculture groundwaters with coefficient of determination (R2) values of 0.947 and 0.901, respectively. Teaching learning-based optimization (TLBO) and adaptive particle swarm optimization (APSO), two of the five soft computing techniques utilized in the study, perform better than the others. Additionally, it was found that remote sensing-based assessment tools and soft computing prediction models were both trustworthy, accurate, and easy to use. Furthermore, these methods could assist in the real-time evaluation of inland aquaculture waters by stakeholders and policymakers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.