Abstract：Image segmentation plays an important role in object-based image analysis. In order to enhance the performance of segmentation method for hierarchical region growing (HRG),this paper proposes a new image segmentation algorithm. The new method consists of three steps: seed determination, seeded region growing (SRG)based over-segmentation (advanced SRG, ASRG) and HRG. To improve the automation and precision of seeds determination, the authors used Gabor texture feature and defined textural homogeneity, attempting to place the seeds at the center of the regions composed of the same texture. At the stage of SRG, spectral information of HRI was combined with shape cues to form a new merging rule to raise the segmentation accuracy and segments compactness of SRG over-segmentation. At the HRG step, an adaptive threshold was used to better retain the multi-scale segmentation property. In the experiment, three scenes of HRI were utilized to validate the proposed method. A supervised segmentation evaluation method was adopted to quantitatively assess the segmentation accuracy of the proposed algorithm, and two state-of-the-art segmentation methods were compared with the proposed method. The experimental results show that the new algorithm proposed in this paper can produce satisfying segmentation.
苏腾飞, 张圣微, 李洪玉. 基于纹理特征与区域生长的高分辨率遥感影像分割算法[J]. 国土资源遥感, 2017, 29(2): 72-81.
SU Tengfei, ZHANG Shengwei, LI Hongyu. Segmentation algorithm based on texture feature and region growing for high-resolution remote sensing image. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 72-81.
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2017, Vol. 29 
