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A novel framework for fine-grained spatio-temporal change detection in satellite images

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Abstract

Change Detection(CD), in the context of remote sensing, determines the differences in different portions of land images when studied over a while. Human change detection and analysis are limited and prone to errors and are incompetent for the scale and speed required for processing satellite data. Automating CD of earth surface features helps humans develop a deeper understanding of the changes in natural phenomena. In this work, we propose a novel framework for detecting the changes in the satellite images using the Siamese based neural network pipeline. Based on Spatio-Temporal analysis, our approach leverages the divide and conquer paradigm to divide the original image into sub-images and then uses the convolution layers to extract the feature maps at a sub-image level to detect fine-grained changes. To evaluate our proposed approach, we used a recently released dataset in 2020, LEVIR-CD, which consists of 637 pairs of high resolution images. In our work, we experimentally establish that decreasing the sub-image size of the original input image increases the accuracy of change detection, with the best performance achieved at 2 × 2 sub-image level with the best recall and F1-score with values 92% and 91.5%, respectively, outperforming the previous best results. Further, we find that our novel framework performs well on different terrains, with varying amounts and types of changes in the satellite image.

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    1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, India

      Riya Agarwal, Shaifali Jindal, Shradha Narain, Rishabh Kaushal & Kalpana Yadav

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    1. Riya Agarwal
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    2. Shaifali Jindal
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    Riya Agarwal, Shaifali Jindal and Shradha Narain are contributed equally to this work.

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    Agarwal, R., Jindal, S., Narain, S. et al. A novel framework for fine-grained spatio-temporal change detection in satellite images. Multimed Tools Appl 83, 1241–1260 (2024). https://doi.org/10.1007/s11042-023-14705-6

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