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Zhuo Wu, Xiaohua Wang, Yongwen Shen, Yueting Shi. Improved Region Merging Algorithm for Remote Sensing Images[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2020, 29(1): 72-79. doi: 10.15918/j.jbit1004-0579.19107
Citation: Zhuo Wu, Xiaohua Wang, Yongwen Shen, Yueting Shi. Improved Region Merging Algorithm for Remote Sensing Images[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2020, 29(1): 72-79.doi:10.15918/j.jbit1004-0579.19107
shu

Improved Region Merging Algorithm for Remote Sensing Images

doi:10.15918/j.jbit1004-0579.19107

  • School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2020-01-06
    Abstract
  • To segment high-resolution remote sensing images (RSIs) accurately on an object level and meet the precise boundary dividing requirement, an improved superpixel segmentation and region merging algorithm is proposed. Simple linear iterative clustering (SLIC) is widely used because of its advantages in performance and effect; however, it causes over-segmentation, which is very disadvantageous to information extraction. In this proposed method, SLIC is firstly adopted for initial superpixel partition. The second stage follows the iterative merging procedure, which uses a hierarchical clustering algorithm and introduces a local binary pattern (LBP) texture feature operator during the process of merging. The experimental results indicate that the proposed method achieved a good segmentation and region merging performance, and worked effectively on cloud detection preprocessing in high-resolution RSIs with cloud and snow overlap situations.
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