Cross-Scene Feature Selection for Hyperspectral Images Based on Cross-Domain Information Gain

Abstract

Feature selection is an important research topic for hyperspectral images (HSIs). It helps to remove the noisy or redundant features. Traditional feature selection algorithms are mostly performed within a single HSI scene (dataset). However, appearance of massive HSIs requires the feature selection problems to be considered across different HSI scenes, e.g., two HSI scenes obtained from different spots or at different time. In this case, the features are not identically distributed within two scenes due to spectral shift. To solve this problem, a cross-scene feature selection algorithm is proposed in this work for HSIs, which is based on cross-domain information gain (CDIG). The main motivation includes two factors, one is the discriminant of selected features to separate different land-cover classes, while the other is the consistency of the selected features between different scenes. Consequently, the proposed CDIG reaches a compromise between aforementioned two factors. Experimental results on two cross-scene HSI datasets show the advantages of the proposed CDIG in cross-scene feature selection problems.