Object-oriented polarimetric SAR image classification via the combination of a pixel-based classifier and a region growing technique

Land-cover type interpretation by the use of remote sensing image classification techniques is always a hot topic. In this paper, an object-oriented method is presented for fully polarimetric synthetic aperture radar (SAR) image classification. Differing from most of the traditional object-oriented classification algorithms, the proposed method employs an innovative classification strategy that combines a pixel-based classifier and a region growing technique. Firstly, taking each individual pixel as a seed pixel, the homogeneous areas are extracted by a region growing technique. Then, using the information of the pixel-based classification result, the pixels located in each homogeneous area are all assigned to a certain class. Finally, the majority voting strategy is deployed to determine the final class label of each pixel. The experiments conducted on two fully polarimetric SAR images reveal that the proposed classification scheme can obtain pleasing classification accuracy and can provide the classification maps with more homogeneous regions than pixel-based classification.

Automated summary

In this paper, an object-oriented method is proposed for fully polarimetric synthetic aperture radar (SAR) image classification. The method combines a pixel-based classifier and a region growing technique to extract homogeneous areas and assign class labels. Experimental results demonstrate that the proposed classification scheme achieves high accuracy and provides classification maps with more homogeneous regions.