RADARSAT Constellation Mission Compact Polarisation SAR Data for Burned Area Mapping with Deep Learning
Yu Zhao, Yifang Ban

TL;DR
This paper explores the use of compact polarisation RADARSAT Constellation Mission SAR data combined with deep learning models for effective burned area mapping, overcoming optical sensor limitations caused by clouds and smoke.
Contribution
It introduces a novel approach utilizing compact-pol SAR data and deep learning for burned area detection, demonstrating significant improvements over traditional methods.
Findings
Compact-pol m-chi decomposition and CpRVI enhance burned area mapping.
Transformer-based models outperform ConvNet-based models.
Best model achieved F1 score of 0.718 and IoU of 0.565.
Abstract
Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. Thus, satellites equipped with Synthetic Aperture Radar (SAR), such as dual-polarization Sentinel-1 and quad-polarization RADARSAT-1/-2 C-band SAR, which can penetrate clouds and smoke, are investigated for mapping burned areas. However, there is limited research on using compact polarisation (compact-pol) C-band RADARSAT Constellation Mission (RCM) SAR data for this purpose. This study aims to investigate the capacity of compact polarisation RCM data for burned area mapping through deep learning. Compact-pol m-chi decomposition and Compact-pol Radar…
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Taxonomy
TopicsMethane Hydrates and Related Phenomena · Coal Properties and Utilization · Earthquake Detection and Analysis
MethodsAttention Is All You Need · Linear Layer · Concatenated Skip Connection · Dense Connections · Max Pooling · Convolution · Multi-Head Attention · U-Net · Residual Connection · 1x1 Convolution
