Optimizing Multi-Class Change Segmentation in High-Resolution Satellite Imagery With a Siamese Network for Low-Resource Environments

This work focuses on multi-class change segmentation to quantify alterations between temporal images. Unlike traditional binary change detection, which requires post-processing to identify specific transformations, we introduce a Siamese architecture called SiamSegCD. This model is designed for dire...

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Bibliographic Details

Published in:	2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) pp. 1 - 4
Main Authors:	Srivastava, Noopur, Jain, Kamal
Format:	Conference Proceeding
Language:	English
Published:	IEEE 02.12.2024
Subjects:	Computational modeling Computer architecture Deep learning Encoderdecoder architectures Feature extraction Image segmentation Multi-class change segmentation Remote sensing Residual neural networks Satellite images Semantics Siamese network Stand-alone self-attention Testing
Online Access:	Get full text
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