Remote sensing image classification based on sparse component analysis

Tingting Cao; Xianchuan Yu; Chunping Yang

doi:10.1117/12.833529

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30 October 2009 Remote sensing image classification based on sparse component analysis

Tingting Cao, Xianchuan Yu, Chunping Yang

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Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74942O (2009) https://doi.org/10.1117/12.833529
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The classification of remote sensing images is a key issue and hot topic in remote sensing image processing domain. Considering that the classification result of classical principle component analysis (PCA) is not satisfying when the spectra of different ground objects are related, a new classification method based on sparse component analysis (SCA) is presented. The proposed method utilizes the sparse characteristic to extract the source signals, and does not demand the sources be independent. The experimental result of TM image shows that compared to the PCA method the overall classification precision of the SCA method enhances approximately 15%, which indicates that the classification result of the SCA method is more reliable and more accurate.

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Tingting Cao, Xianchuan Yu, and Chunping Yang "Remote sensing image classification based on sparse component analysis", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74942O (30 October 2009); https://doi.org/10.1117/12.833529

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