Details

Autor(en) / Beteiligte

• Xudong Kang
• Shutao Li
• Leyuan Fang
• Benediktsson, Jon Atli

Titel

Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images

Ist Teil von

• IEEE transactions on geoscience and remote sensing, 2015-04, Vol.53 (4), p.2241-2253

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2015

Quelle

IEL

Beschreibungen/Notizen

• 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.