Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 94; pp. 25 - 36
• Main Authors: Li, Jiayi, Zhang, Hongyan, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2014; Elsevier
• Subjects: Algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Classification; Classifiers; Column generation; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Hyperspectral image classification; hyperspectral imagery; Internal geophysics; Joint collaboration model; Kernel method; Kernels; Pixels; regression analysis; Representations; Similarity; Spectra; support vector machines; Teledetection and vegetation maps; Joint collaboration model; Column generation; Kernel method; Hyperspectral image classification; experimental studies; algorithms; models; maps; joints; classification; Image; signals; regression; imagery; performances; Pixel
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2014.04.014

We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyperspectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixels as a feature, and then utilizes a nonlocal joint collaborative regression model for kernel signal reconstruction and the subsequent pixel classification. We also develop two kinds of specific radial basis function kernels for measuring the similarities. The experimental results indicate that the proposed algorithms obtain a competitive performance and outperform other state-of-the-art regression-based classifiers and the classical support vector machines classifier.