An Agglomerative Hierarchical Clustering Based High-Resolution Remote Sensing Image Segmentation Algorithm

2008 ◽  
Author(s):  
Liu Rongjie ◽  
Zhang Jie ◽  
Song Pingjian ◽  
Shao Fengjing ◽  
Liu Guanfeng
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Hierarchical Clustering ◽  
Remote Sensing Image ◽  
Segmentation Algorithm ◽  
Agglomerative Hierarchical Clustering ◽  
Image Segmentation Algorithm

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.

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

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
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