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An Improved Hybrid Segmentation Method for Remote Sensing Images
Article

Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

by 1,2, 2,*, 2,3 and 4
1
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz, Amr Abd-Elrahman, Zoltan Szantoi and Tao Liu
ISPRS Int. J. Geo-Inf. 2021, 10(6), 420; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060420
Received: 23 February 2021 / Revised: 17 June 2021 / Accepted: 18 June 2021 / Published: 20 June 2021
Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects. View Full-Text
Keywords: GEOBIA; image segmentation; parameter optimization; semantic geo-object; Gaofen-1 images GEOBIA; image segmentation; parameter optimization; semantic geo-object; Gaofen-1 images
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MDPI and ACS Style

Wang, J.; Jiang, L.; Qi, Q.; Wang, Y. Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images. ISPRS Int. J. Geo-Inf. 2021, 10, 420. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060420

AMA Style

Wang J, Jiang L, Qi Q, Wang Y. Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images. ISPRS International Journal of Geo-Information. 2021; 10(6):420. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060420

Chicago/Turabian Style

Wang, Jun, Lili Jiang, Qingwen Qi, and Yongji Wang. 2021. "Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images" ISPRS International Journal of Geo-Information 10, no. 6: 420. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060420

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