Object-Oriented Unsupervised Classification of PolSAR Images Based on Image Block

Abstract

Land Use and Land Cover (LULC) classification is one of the tasks of Polarimetric Synthetic Aperture Radar (PolSAR) images’ interpretation, and the classification performance of existing algorithms is highly sensitive to the class number, which is inconsistent with the reality that LULC classification should have multiple levels of detail in the same image. Therefore, an object-oriented unsupervised classification algorithm for PolSAR images based on the image block is proposed. Firstly, the image is divided into multiple non-overlapping image blocks, and h/q/gray-Wishart classification is performed in each block. Secondly, each cluster obtained is regarded as an object, and the affinity matrix of objects is calculated in the global image. Finally, the objects are merged into the specified class number by density peak clustering (DPC), and the adjacent objects at the block boundary are checked and forced to merge. Experiments are carried out with the measured data of the airborne AIRSAR and E-SAR and the spaceborne GF-3. The experimental results show that the proposed algorithm achieves good classification results under a variety of class numbers.