DocumentCode :
1404675
Title :
An Object-Oriented Semantic Clustering Algorithm for High-Resolution Remote Sensing Images Using the Aspect Model
Author :
Yi, Wenbin ; Tang, Hong ; Chen, Yunhao
Author_Institution :
State Key Lab. of Earth Surface Processes & Resource Ecology, Beijing Normal Univ., Beijing, China
Volume :
8
Issue :
3
fYear :
2011
fDate :
5/1/2011 12:00:00 AM
Firstpage :
522
Lastpage :
526
Abstract :
In this letter, we present a novel object-oriented semantic clustering algorithm for high-spatial-resolution remote sensing images using the probabilistic latent semantic analysis (PLSA) model coupled with neighborhood spatial information. First of all, an image collection is generated by partitioning a large satellite image into densely overlapped subimages. Then, the PLSA model is employed to model the image collection. Specifically, the image collection is partitioned into two subsets. One is used to learn topic models, where the number of topics is determined using a minimum description length criterion. The other is folded in using the learned topic models. Therefore, every pixel in each subimage has been allocated a topic label. At last, the cluster label of every pixel in the large satellite image is derived from the topic labels of multiple subimages which cover the pixel in the image collection. Experimental results over a QUICKBIRD image show that the clusters of the proposed algorithm are better than K-means and Iterative Self-Organizing Data Analysis Technique Algorithm in terms of object-oriented property.
Keywords :
data analysis; geophysical image processing; image resolution; iterative methods; pattern clustering; probability; remote sensing; QUICKBIRD image; aspect model; high-resolution remote sensing images; image collection; iterative selforganizing data analysis technique algorithm; learn topic models; minimum description length criterion; neighborhood spatial information; object-oriented semantic clustering algorithm; probabilistic latent semantic analysis model; topic label; Algorithm design and analysis; Clustering algorithms; Entropy; Object oriented modeling; Pixel; Remote sensing; Semantics; Neighborhood semantic information; object oriented; probabilistic latent semantic analysis; semantic clustering;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2010.2090034
Filename :
5668906
Link To Document :
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