Remote sensing images recognition based on constrained independent component analysis via compressed sensing

Jinhui Lan; Yiliang Zeng; Yifang Lu

doi:10.1117/12.918828

8 June 2012 Remote sensing images recognition based on constrained independent component analysis via compressed sensing

Jinhui Lan, Yiliang Zeng, Yifang Lu

Author Affiliations +

Proceedings Volume 8365, Compressive Sensing; 83650X (2012) https://doi.org/10.1117/12.918828
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

According to the feature of strong correlation of remote sensing image, a target recognition method based on Constrained Independent Component Analysis (CICA) via Compressed Sensing is put forward to realize the goal of remote sensing image recognition. By using abundance nonnegative restriction and the abundance sum-to-one constraint, an Adaptive Abundance Modeling (AAM) algorithm is proposed to ensure the reliability of the objective function. Then the CS feature space classifier based on Constrained Independent Component Analysis of sparse signal is established, so as to achieve recognition quickly. Experimental results show that the proposed algorithm can obtain more accurate results as high as 90%, and improve the timeliness effectively.

Citation Download Citation

Jinhui Lan, Yiliang Zeng, and Yifang Lu "Remote sensing images recognition based on constrained independent component analysis via compressed sensing", Proc. SPIE 8365, Compressive Sensing, 83650X (8 June 2012); https://doi.org/10.1117/12.918828

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