Band Selection for Change Detection from Hyperspectral Images

In this paper, we propose to apply unsupervised band selection to improve the performance of change detection in multitemporal hyperspectral images (HSI-CD). By reducing data dimensionality through finding the most distinctive and informative bands in the difference image, foreground changes may be better detected. Band selection-based dimensionality reduction (BS-DR) technique is considered to investigate in details the following sub-problems in HSI-CD including: 1) the estimated number of multi-class changes; 2) the binary CD; 3) the multiple CD; 4) the change discriminability; 5) the optimal number of selected bands. Thus it contributes at first time a quantitative analysis of the BS-DR approach impacting on the HSI-CD performance. Due to the difficulty of having training samples in an unknown environment, unsupervised band selection and change detection are considered. A pair of real multitemporal hyperspectral Hyperion data set has been used to validate the proposed approach. Experimental results confirmed the effectiveness of selecting a band subset to obtain a satisfactory CD result, comparing with the one using original full bands. In addition, the results also demonstrated that the reduced feature space is capable to maintain sufficient information for detecting the occurred spectrally significant changes. CD performance is enhanced with respect to the increasing of change representative and discriminable capabilities.