Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 53; no. 4; pp. 2241 - 2253
• Main Authors: Xudong Kang, Shutao Li, Leyuan Fang, Benediktsson, Jon Atli
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Bands; Classification; Decomposition; Educational institutions; Feature extraction; hyperspectral image; Hyperspectral imaging; Image classification; image fusion; intrinsic image decomposition (IID); Materials; Reflectance; Reflectivity; Spectra; support vector machines (SVMs); Training; hyperspectral image; Feature extraction; intrinsic image decomposition (IID); image fusion; support vector machines (SVMs)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2014.2358615

In this paper, a novel feature extraction method based on intrinsic image decomposition (IID) is proposed for hyperspectral image classification. The proposed method consists of the following steps. First, the spectral dimension of the hyperspectral image is reduced with averaging-based image fusion. Then, the dimension reduced image is partitioned into several subsets of adjacent bands. Next, the reflectance and shading components of each subset are estimated with an optimization-based IID technique. Finally, pixel-wise classification is performed only on the reflectance components, which reflect the material-dependent properties of different objects. Experimental results show that, with the proposed feature extraction method, the support vector machine classifier is able to obtain much higher classification accuracy even when the number of training samples is quite small. This demonstrates that IID is indeed an effective way for feature extraction of hyperspectral images.