Sentinel-1-unsupervised-burned-area-detection

This code represents a processing workflow for the unsupervised detection and mapping of burned areas using SAR Sentinel-1 (S-1) satellite data (https://sentinel.esa.int/web/sentinel/missions/sentinel-1). It was developed by implementing ESA-snappy (https://senbox.atlassian.net/wiki/spaces/SNAP/pages/24051769/Cookbook) for image pre-processing and Scikit-learn (https://scikit-learn.org/) for processing and classification. It consists of the following fundamental main steps: [A] using ESA-snappy: 1) open raw S-1 data; 2) S-1 pre-processing; backscatter Time-Average for pre- and post-fire period; 3) calculation of the SAR indices [burn difference (RBD), the logarithmic RADAR burn ratio (LogRBR), the delta radar vegetation index (ΔRVI) and the dual dual-polarization SAR vegetation index ΔDPSVI); 4) gray-level co-occurrence matrix (GLCM) texture features calculation. [B] Using Scikit-learn: 5) principal components analysis (PCA) transformation; 6) silhouette score analysis to set the k parameter value; 7) unsupervised classification using the k-means algorithm.