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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| Published in: | 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) pp. 1 - 4 |
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| Main Authors: | , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
02.12.2024
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| Abstract | 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 direct segmentation of land-use and land-cover changes, particularly suited for low-resource environments. SiamSegCD utilizes deep learning models like UNet and DeepLabv3+ with a lightweight ResNet-50 backbone and stand-alone self-attention to yield a fully self-attentional model, implemented in a Siamese twin framework. The model's performance is assessed using the HRSCD dataset, revealing better results with the DeepLabv3+ encoderdecoder compared to the UNet-based version, which demonstrated a 5.98% improvement in mean Intersection over Union (mIoU). |
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| AbstractList | 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 direct segmentation of land-use and land-cover changes, particularly suited for low-resource environments. SiamSegCD utilizes deep learning models like UNet and DeepLabv3+ with a lightweight ResNet-50 backbone and stand-alone self-attention to yield a fully self-attentional model, implemented in a Siamese twin framework. The model's performance is assessed using the HRSCD dataset, revealing better results with the DeepLabv3+ encoderdecoder compared to the UNet-based version, which demonstrated a 5.98% improvement in mean Intersection over Union (mIoU). |
| Author | Srivastava, Noopur Jain, Kamal |
| Author_xml | – sequence: 1 givenname: Noopur surname: Srivastava fullname: Srivastava, Noopur organization: Indian Institute of Technology,Geomatics Group,Department of Civil Engineering,Roorkee,Uttarakhand,India,247667 – sequence: 2 givenname: Kamal surname: Jain fullname: Jain, Kamal organization: Indian Institute of Technology,Geomatics Group,Department of Civil Engineering,Roorkee,Uttarakhand,India,247667 |
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| Snippet | This work focuses on multi-class change segmentation to quantify alterations between temporal images. Unlike traditional binary change detection, which... |
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| SubjectTerms | 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 |
| Title | Optimizing Multi-Class Change Segmentation in High-Resolution Satellite Imagery With a Siamese Network for Low-Resource Environments |
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