FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING
Keywords: Crop type, RADARSAT 2, Polarimetry, Machine learning, Random Forest, Cat Boost
Abstract. Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected.