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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) s. 1 - 4
Hlavní autoři: Srivastava, Noopur, Jain, Kamal
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 02.12.2024
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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).
DOI:10.1109/InGARSS61818.2024.10984419