Matrix-Based Discriminant Subspace Ensemble for Hyperspectral Image Spatial-Spectral Feature Fusion

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 2; pp. 783 - 794
• Main Authors: Hang, Renlong, Liu, Qingshan, Song, Huihui, Sun, Yubao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Contextual spatial-spectral feature fusion; Discriminant analysis; Feature extraction; hyperspectral (HS) image classification; Image classification; Kernel; matrix-based discriminant analysis (MDA); Preserves; random sampling; Remote sensing; Sampling techniques; Sensors; Spatial resolution; Subspaces; support vector machine (SVM); Support vector machines; Training; random sampling; hyperspectral (HS) image classification; support vector machine (SVM); matrix-based discriminant analysis (MDA); Contextual spatial–spectral feature fusion
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2015.2465899

Spatial-spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial-spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial-spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method.