Abstract
Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.
Highlights
Image segmentation is an important pre-processing step in the object-based image analysis (OBIA) technology
binary partition trees (BPT) segmentation based on generalized statistical region merging (GSRM) superpixels (GSRM-BPT)for polarimetric SAR (PolSAR) image
The pixel-based BPT (P-BPT) algorithm and the GSRM-BPT algorithm are applied to the ESAR data for multiscale segmentation respectively
Summary
Image segmentation is an important pre-processing step in the object-based image analysis (OBIA) technology. Alonso-Gonzndezlez et al (2010, 2011, 2012) proposed a polarimetric SAR (PolSAR) image BPT segmentation algorithm. This algorithm has good potential in OBIA since it can keep image details on small scale as well as reveal the contour information of large homogeneous regions on large scale. This algorithm is inefficient and can be affected by the speckle noise since the BPT is built on single pixels. We proposed a multiscale segmentation algorithm by combing the BPT segmentation and a superpixel segmentation algorithm
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More From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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