ICA-based multi-temporal multi-spectral remote sensing images change detection

Juan Gu; Xin Li; Chunlin Huang; Yiu Yu Ho

doi:10.1117/12.783807

15 April 2008 ICA-based multi-temporal multi-spectral remote sensing images change detection

Juan Gu, Xin Li, Chunlin Huang, Yiu Yu Ho

Proceedings Volume 6960, Space Exploration Technologies; 69600R (2008) https://doi.org/10.1117/12.783807
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Change detection is the process of identifying difference in the scenes of an object or a phenomenon, by observing the same geographic region at different times. Many algorithms have been applied to monitor various environmental changes. Examples of these algorithms are difference image, ratio image, classification comparison, and change vector analysis. In this paper, a change detection approach for multi-temporal multi-spectral remote sensing images, based on Independent Component Analysis (ICA), is proposed. The environmental changes can be detected in reduced second and higher-order dependencies in multi-temporal remote sensing images by ICA algorithm. This can remove the correlation among multi-temporal images without any prior knowledge about change areas. Different kinds of land cover changes are obtained in these independent source images. The experimental results in synthetic and real multi-temporal multi-spectral images show the effectiveness of this change detection approach.

Citation Download Citation

Juan Gu, Xin Li, Chunlin Huang, and Yiu Yu Ho "ICA-based multi-temporal multi-spectral remote sensing images change detection", Proc. SPIE 6960, Space Exploration Technologies, 69600R (15 April 2008); https://doi.org/10.1117/12.783807

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KEYWORDS

Independent component analysis

Remote sensing

Earth observing sensors

Landsat

Image classification

Principal component analysis

Reflectivity

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