scholarly journals Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

2018 ◽  
Vol 10 (5) ◽  
pp. 781 ◽  
Author(s):  
Yongji Wang ◽  
Qingyan Meng ◽  
Qingwen Qi ◽  
Jian Yang ◽  
Ying Liu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Remote Sensing Image ◽  
Region Merging ◽  
Segmentation Method

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

HIGH-RESOLUTION REMOTE SENSING IMAGE SEGMENTATION METHOD BASED ON SReLU

2019 ◽  
Vol 34 (3) ◽  
Author(s):  
Chenming Li ◽  
Xiaoyu Qu ◽  
Yao Yang ◽  
Hongmin Gao ◽  
Yongchang Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Segmentation Method

Machine learning-assisted region merging for remote sensing image segmentation

2020 ◽  
Vol 168 ◽  
pp. 89-123 ◽  
Author(s):  
Tengfei Su ◽  
Tingxi Liu ◽  
Shengwei Zhang ◽  
Zhongyi Qu ◽  
Ruiping Li
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Image Segmentation ◽  
Remote Sensing Image ◽  
Region Merging

Remote Sensing Image Segmentation Based on Improved Statistical Region Merging

2014 ◽  
Vol 667 ◽  
pp. 226-229
Author(s):  
Xiu Li Gong ◽  
Zhi Ming Wang
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Remote Sensing Image ◽  
Segmentation Algorithm ◽  
Region Merging ◽  
Gradient Estimation ◽  
Coarse To Fine ◽  
Statistical Region Merging ◽  
Image Segmentation Algorithm ◽  
Dissimilarity Criterion

Statistical Region Merging (SRM) is an efficient image segmentation algorithm for images with noise and partial occlusion. However, due to the complexity of remote sensing image, SRM can’t give satisfactory results. This paper proposes an improved image segmentation algorithm for remote sensing image based on SRM. Firstly, 8-connexity gradient estimation models are used to obtain more precisely edges. Secondly, the dissimilarity criterion between regions is replaced by a normalized distance standard. Finally, it dynamically updates and sorts dissimilarity between regions during region merging. Experimental results show the proposed algorithm can achieve better segmentation results from coarse to fine compared with original SRM.

Sign in / Sign up

Export Citation Format

Share Document