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An Improved Hybrid Segmentation Method for Remote Sensing Images

by Jun Wang 1,2, Lili Jiang 2,*, Yongji Wang 2,3 and Qingwen Qi 2
1
College of 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
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 543; https://doi.org/10.3390/ijgi8120543
Received: 28 October 2019 / Revised: 23 November 2019 / Accepted: 27 November 2019 / Published: 28 November 2019
Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).
Keywords: segmentation; watershed; GF-1 images; fast lambda-schedule; common boundary length penalty segmentation; watershed; GF-1 images; fast lambda-schedule; common boundary length penalty
MDPI and ACS Style

Wang, J.; Jiang, L.; Wang, Y.; Qi, Q. An Improved Hybrid Segmentation Method for Remote Sensing Images. ISPRS Int. J. Geo-Inf. 2019, 8, 543.

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