PolSAR Data-Based Land Cover Classification Using Dual-Channel Watershed Region-Merging Segmentation and Bagging-ELM

Land cover classification based on spaceborne synthetic aperture radar (SAR) imagery is an emerging technology with many applications. Machine learning and related ensemble models have attracted much attention to this technology. This letter presents a new object-oriented land cover classification based on Polarimetric SAR data that integrate the dual-channel (DC) Watershed region-merging segmentation algorithm and a Bagging-based ensemble extreme learning machine (Bagging-ELM) image object classifier. The advantage of the DC segmentation algorithm is verified by comparing it with the well-known simple linear iterative clustering (SLIC) superpixel algorithm. Following the DC segmentation, the Bagging-ELM is implemented to perform the classification of image objects. Besides, two other classification methods (single ELM and ensembled-tree algorithms) are used for performance comparison. The quantitative analysis with respect to ground-truth information available for the test sites shows that the proposed approach can achieve noticeable improvements in classification accuracy and outperforms the other two algorithms, showing the potential for spaceborne-SAR-based land cover classification task in real applications.