Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (5): 658-667.doi: 10.11947/j.AGCS.2022.20210423

• Photogrammetry and Remote Sensing • Previous Articles     Next Articles

The automatic determination method of the optimal segmentation result of high-spatial resolution remote sensing image

CHENG Jiehai, HUANG Zhongyi, WANG Jianru, HE Shi   

  1. School of Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
  • Received:2021-07-30 Revised:2022-03-14 Online:2022-05-20 Published:2022-05-28
  • Supported by:
    The National Natural Science Foundation of China (No. 42171299);The Natural Science Foundation of Henan (No. 162300410122);The Science and Technology Research Project of Henan (Nos. 212102311149;222102320341);The Key Scientific Research Project of Colleges and Universities of Henan (No. 22B420004);The Fundamental Research Fund for the Universities of Henan (No. NSFRF210401)

Abstract: The existing methods cannot fully take into account the multi-band spectral information of remote sensing images, and ignore the multi-scale characteristics of geographical elements in remote sensing images. This study proposed an unsupervised evaluation method for automatically determining the optimal segmentation result of high-spatial resolution remote sensing image. This method generates the spectral information divergence based on information entropy, and uses the spectral information divergence to construct the indexes that can express the intra-segment homogeneity and inter-segment heterogeneity. Based on the constructed homogeneity and heterogeneity indexes, the strategy of "rough estimation + fine determination" is adopted to gradually obtain an optimal image segmentation result after multi-level optimization. The proposed method was carried out in three different underlying surface image areas. Experimental results demonstrate that the method can effectively automatically determine the optimal segmentation results of high-spatial resolution remote sensing images. Compared with existing methods, the optimal image segmentation results determined by the method have higher quality and are closer to the reference segmentation results.

Key words: high spatial resolution remote sensing image, geographic object-based image analysis, optimal image segmentation, information entropy, spectral information divergence

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