An Adaptive Multiview Active Learning Approach for Spectral-Spatial Classification of Hyperspectral Images

Combining spectral and spatial features in hyperspectral image classification is a common practice due to the improvements in classification accuracy that can be obtained by extracting information from neighboring pixels. However, the resulting high dimensionality of the input data and the typically limited number of labeled samples are two key challenges that affect the overall performance of supervised classification methods. To alleviate these two issues, we propose an adaptive multiview (MV)-based active learning (AL) approach that is different from the existing MV AL methods in two main ways: 1) to improve the view sufficiency, a spectral-spatial view generation approach is proposed by incorporating spatial features derived from the segmentation maps into each view and 2) to increase the diversity across views, a dynamic view is generated at each AL iteration by selecting important features from the predefined views. The performance of each view is further improved by applying the proposed AL algorithm in conjunction with an ensemble approach as back-end classifier, a scenario less explored in the remote sensing community than single classifier-based AL methodologies. The proposed approach is applied to three widely analyzed hyperspectral data sets [i.e., Kennedy Space Center (KSC), Indian Pine, and University of Houston (UH)], and the results demonstrate the efficacy of the proposed method compared with other state-of-the-art AL classification methods.