Remote Sensing Image Segmentation Based on and Cloud Model and Parallel Mechanism

2011 ◽  
Vol 341-342 ◽  
pp. 710-713
Author(s):  
Yan Li Liu ◽  
Hua Jiang ◽  
Gang Xu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Parallel Mechanism ◽  
Cloud Model ◽  
Remote Sensing Image ◽  
Segmentation Algorithms ◽  
Traditional Image

This paper discusses the uncertainty in remote sensing image segmentation by studying the uncertainty in cloud model , qualitative to quantitative conversion, proposed one way to study the image segmentation based on the combination of Parallel mechanism and cloud mode. Proved by experiments, this method can better describe the uncertainty of image target. It can quickly and accurately segment the target, the efficiency is far superior then the traditional image segmentation algorithms.

scholarly journals Multi-spectral Texture Characterisation for Remote Sensing Image Segmentation

2009 ◽  
pp. 257-264 ◽  
Author(s):  
Filiberto Pla ◽  
Gema Gracia ◽  
Pedro García-Sevilla ◽  
Majid Mirmehdi ◽  
Xianghua Xie
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Remote Sensing Image

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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