Despite the low spatial resolution of these Earth observations, these sensors provide helpful information for detecting fires and burned areas with high frequency. These preliminary maps are the low-accuracy burn mapping sometimes used for the initial finding of fire locations before using other data with higher precision. These works include large-scale wildfire monitoring and preliminary mapping with coarse spatial resolution satellite data, such as Advanced Very-High-Resolution Radiometer (AVHHR) and Moderate-Resolution Imaging Spectroradiometer (MODIS). Several research works have shown the potential of multispectral satellite data for fire and burned area detection and mapping. Optical satellite data have been widely used to monitor and map this phenomenon. Satellite remote sensing technology provides valuable information for monitoring Earth’s land covers, including burned areas caused by wildfires. Moreover, the proposed model achieved superior accuracy of 87.67% (i.e., more than 2% improvement) compared to other saliency-guided techniques, including SVM and DNN. This method significantly improved the burn detection ability of non-saliency-guided models. The developed approach based on the SG-FCM-DCNN model was investigated to map the burned area of Rossomanno-Grottascura-Bellia, Italy. These regions are not affected by noise and can improve the model performance. This method defines salient regions with a high probability of being burned. Furthermore, a saliency-guided (SG) approach was deployed to reduce false detections and SAR image speckles. To overcome this problem, we proposed an unsupervised method that derives DCNN training data from fuzzy c-means (FCM) clusters with the highest and lowest probability of being burned. However, labeled ground truth might not be available in many cases or requires professional expertise to generate them via visual interpretation of aerial photography or field visits. Accurate mapping with DCNN techniques requires high quantity and quality training data. In this study, we assess the potential of Sentinel-1 SAR images for precise forest-burned area mapping using deep convolutional neural networks (DCNN). SAR data provide sufficient information for burned area detection in any weather condition, making it superior to optical data.
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