Feature selection for cross-scene hyperspectral image classification using cross-domain ReliefF

Abstract

Hyperspectral images (HSIs) have hundreds of narrow and adjacent spectral bands, which will result in feature redundancy, decreasing the classification accuracy. Feature (band) selection helps to remove the noisy or redundant features. Most traditional feature selection algorithms can be only performed on a single HSI scene. However, appearance of massive HSIs has placed a need for joint feature selection across different HSI scenes. Cross-scene feature selection is not a simple problem, since spectral shift exists between different HSI scenes, even though the scenes are captured by the same sensor. The spectral shift makes traditional single-dataset-based feature selection algorithms no longer applicable. To solve this problem, we extend the traditional ReliefF to a cross-domain version, namely, cross-domain ReliefF (CDRF). The proposed method can make full use of both source and target domains and increase the similarity of samples belonging to the same class in both domains. In the cross-scene classification problem, it is necessary to consider the class-separability of spectral features and the consistency of features between different scenes. The CDRF takes into account these two factors using a cross-domain updating rule of the feature weights. Experimental results on two cross-scene HSI datasets show the superiority of the proposed CDRF in cross-scene feature selection problems.