scholarly journals Remote Sensing Image Segmentation Algorithm Based on Multi-agent and Fuzzy Clustering

2017 ◽  
Author(s):  
Lei LIU ◽  
Lin-li ZHOU ◽  
Hui-fang BAO
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Remote Sensing Image ◽  
Segmentation Algorithm ◽  
Multi Agent ◽  
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 REGION-BASED FUZZY CLUSTERING IMAGE SEGMENTATION ALGORITHM WITH KULLBACK-LEIBLER DISTANCE

2020 ◽  
Vol V-4-2020 ◽  
pp. 27-31
Author(s):  
X. L. Li ◽  
J. S. Chen
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Spatial Constraints ◽  
Image Domain ◽  
Markov Random ◽  
Optimal Segmentation ◽  
Image Segmentation Algorithm

Abstract. To effectively describe the uncertainty of remote sensing image segmentation, a novel region-based algorithm using fuzzy clustering and Kullback-Leibler (KL) distance is proposed. By regular tessellation, the image domain is completely divided into several sub-blocks to overcome the complex noise existed in high-resolution remote sensing images. Taking the blocks as the basic processing units, KL divergence is used to model the distance between blocks and clusters, which enables the model to describe the uncertainty of the non-similarity relationship. Besides, based on the theory of Markov Random Field (MRF), the regionalized KL entropy regularization term is established and added to the objective function to further consider the spatial constraints. Finally, the optimal segmentation results are obtained by estimating the parameters. The experiments carried out on different kinds of remote sensing images by comparing algorithms fully demonstrate the performance of the proposed algorithm.

scholarly journals Large Remote Sensing Image Segmentation with Stitching Strategy Based on Dominant Color

2021 ◽  
pp. 67-79
Author(s):  
Haizhong Zhang ◽  
◽  
Ligang Wang ◽  
Fei Tong
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Similarity Measure ◽  
Computer Experiments ◽  
Remote Sensing Image ◽  
Segmentation Algorithm ◽  
Object Based Image Analysis ◽  
Dominant Color ◽  
Object Based ◽  
Color Similarity

Large remote sensing image segmentation is a crucial issue in object-based image analysis. It is common sense that a segmentation framework consists of three components: (1) dividing largeremote sensing image into blocks for overcoming the constraint of computer memory; (2) executing segmentation algorithm for each block individually; (3) stitching segmentation results of all blocks into a complete result for eliminating artificial borderscreated by dividing blocks. However, there is a lack of mature technologies to eliminate artificial borders produced by dividing blocks. In this paper, we proposed a new stitching strategy based on the dominant color similarity measure and modified thetraditional methodof dominant color similarity measure to make itmoresuitable for measuring the similarity of two segmented regions. A multi-scale segmentation algorithm is adopted for segmenting each block. External memory is used to store intermediate segmentation results and exchange data with internal memory. We tested the algorithm with three different images and validated that the algorithm can implement the segmentation for large remote sensing images in a common computer. Experiments demonstrate that the stitchingstrategy based on the similarity measure of dominant color can effectively eliminate artificial borders.

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