Browsing by Author "Basavaraju, K.S."

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An Effective Deep Learning Model for Pan-Sharpening of Satellite Images
(Institute of Electrical and Electronics Engineers Inc., 2024) Telang, S.; Basavaraju, K.S.; Sravya, N.; Lal, S.
Image fusion techniques are widely used to enhance images by combining two or more remote sensing images. The fusion task of "pan-sharpening"is to merge low resolution Multispectral (MS) and High resolution Panchromatic (PAN) satellite images of the same scene obtained by the same satellite. This paper presents proposed an effective deep learning model leveraging a combination of novel techniques for feature enhancement and aggregation. The proposed model named as Efficient Non-local Feature Enhancement Network (ENFE-Net) integrates the PAN guided band-aware feature enhancement module with an Efficient Non-local Attention (ENLA) mechanism and Spectral Aggregation Module (SpecAM). The PAN guided band-aware feature enhancement module facilitates effective feature extraction, leverages PAN features to conduct band-aware multi-spectral feature modulation, selectively enhancing the information of each spectral band. Additionally, the integration of the ENLA mechanism enables the model to capture similar contextual dependencies in the input data efficiently, enhancing its discriminative power. Furthermore, the SpecAM is employed to aggregate spectral information effectively, improving the model's effectiveness to adjust the spectral information. Performance of proposed ENFE-Net model is evaluated on PAirMax datasets and demonstrate its superior performance compared to existing traditional and recent deep learning methods. Experimental results of proposed ENFE-Net model show significant improvements over existing pan-sharpening methods. Â© 2024 IEEE.
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.
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. Â© 2024 IEEE.
Recent Advances in Urban Expansion Monitoring Through Deep Learning-Based Semantic Change Detection Techniques From Satellite Imagery
(Institute of Electrical and Electronics Engineers Inc., 2024) Basavaraju, K.S.; Sravya, N.; Kevala, V.D.; Lal, S.
Urban expansion monitoring is essential for understanding and managing the dynamic growth of cities. Recently, deep learning (DL)-based semantic change detection (SCD) techniques have emerged as powerful tools for accurately monitoring urban expansion using satellite imagery. This paper offers comprehensive overview of the recent advancements in urban expansion monitoring through DL-based SCD techniques. It covers various publicly available SCD datasets and assesses performance, advantages, and limitations of existing DL-based SCD architectures, categorized into three types. Furthermore, the paper discusses the challenges encountered in DL-based SCD techniques. Finally, it outlines future research directions in urban expansion monitoring using DL-based SCD techniques. Â© 2024 IEEE.
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.
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.