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 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.