Texture segmentation in remote sensing images by means of packet wavelets and fuzzy clustering

1995 ◽  
Author(s):  
A. Mecocci ◽  
Paolo Gamba ◽  
Andrea Marazzi ◽  
Mauro Barni
Keyword(s):  
Remote Sensing ◽  
Fuzzy Clustering ◽  
Texture Segmentation ◽  
Remote Sensing Images

Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images

2019 ◽  
Vol 47 (3) ◽  
pp. 427-437 ◽  
Author(s):  
Sumit Kaur ◽  
R. K. Bansal ◽  
Mamta Mittal ◽  
Lalit Mohan Goyal ◽  
Iqbaldeep Kaur ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fuzzy Clustering ◽  
Remote Sensing Images ◽  
Mixed Pixel

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.

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

2011 ◽  
Vol 181 (4) ◽  
pp. 699-715 ◽  
Author(s):  
Ashish Ghosh ◽  
Niladri Shekhar Mishra ◽  
Susmita Ghosh
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Remote Sensing Images
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