Remote Sensing Image Segmentation of Ulan Buh Desert Based on Mathematical Morphology

2011 ◽  
Vol 268-270 ◽  
pp. 1332-1338
Author(s):  
Feng Ri Li ◽  
He Zhi Wang ◽  
Wei Wei Jia ◽  
Zhao Gang Liu

As the basis of 1:10000 color aerial remote sensing image of Ulan Buh Desert, Inner Mongolia, this paper studies on white thorn which is a typical representative of the nature of scarcity and mass organizations like the vegetation in the desert region. By using mathematical morphology theory and the Matlab 7.0 software, the white thorn will be conducted from the remote sensing image segmentation. Comparing the conducted image with the outer shape of the industry survey results, the accuracy of segmentation could be verified by the gray-level co-occurrence matrix comparisons. This research provides the technical reference for the large sample survey of desert region by remote sensing image segmentation.

Author(s):  
Filiberto Pla ◽  
Gema Gracia ◽  
Pedro García-Sevilla ◽  
Majid Mirmehdi ◽  
Xianghua Xie

Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu

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.


Author(s):  
Chenming Li ◽  
Xiaoyu Qu ◽  
Yao Yang ◽  
Hongmin Gao ◽  
Yongchang Wang ◽  
...  

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