NDLI: A novel approach for satellite image classification using local self-similarity

Content Provider	IEEE Xplore Digital Library
Author	Zheng Huaxin Bai Xiao Zhao Huijie
Copyright Year	2011
Description	Author affiliation: Beihang University, 37 Xueyuan Road, Haidian District, Beijing, China, 100191 (Zheng Huaxin; Bai Xiao; Zhao Huijie)
Abstract	Extracting man-made objects in satellite images which are generated from the meter to sub-meter resolution plays an important role in remote satellite image analysis. However, spectral characteristics of urban land objects are so similar. So the classification accuracies are far from satisfactory by using only spectral information. As a result, researchers turn to incorporate geometrical information into satellite image classification. In this paper, we introduce a new local feature, namely local self-similarity(LSS) which captures internal geometric layouts of local self-similarities, into high spatial resolution images classification application. Our method captures self-similarity of color, edges, repetitive patterns and complex textures in a single unified way. With the help of Bag-of-Visual Words and SVMs, the proposed method performs well. Experimental results on Quickbird-image data set show that the proposed local self-similarity representation yields better classification performance than the low-level features, such as the spectral and texture features.
Starting Page	2888
Ending Page	2891
File Size	471994
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049818
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Remote sensing Feature extraction Visualization Satellites Spatial resolution Support vector machines Local self-similarity Classification High resolution imagery
Content Type	Text
Resource Type	Article

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