On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification

• Published in: IEEE transactions on geoscience and remote sensing Vol. 50; no. 3; pp. 879 - 893
• Main Authors: Zhang, Lefei, Zhang, Liangpei, Tao, Dacheng, Huang, Xin
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied geophysics; Classification; Computational complexity; dimensional reduction; Earth sciences; Earth, ocean, space; Exact sciences and technology; Feature extraction; hyperspectral; Hyperspectral imaging; Internal geophysics; multiple features; Optimization; Shape; experimental studies; textures; hyperspectral; remote sensing; classification; collections; imagery; dimensional reduction; reduction; performances; frame structure; Pixel; multiple features
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2011.2162339

In hyperspectral remote sensing image classification, multiple features, e.g., spectral, texture, and shape features, are employed to represent pixels from different perspectives. It has been widely acknowledged that properly combining multiple features always results in good classification performance. In this paper, we introduce the patch alignment framework to linearly combine multiple features in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function. This scheme considers the specific statistical properties of each feature to achieve a physically meaningful unified low-dimensional representation of multiple features. Experiments on the classification of the hyperspectral digital imagery collection experiment and reflective optics system imaging spectrometer hyperspectral data sets suggest that this scheme is effective.