Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Basavaraju, K.S.

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    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.
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    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. © 2024 IEEE.
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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. © 2024 IEEE.