Abstract
Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the “internal uncertainty” of each independent view, and adopt the inconsistencies between views to estimate the “external uncertainty”. Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.
Highlights
Hyperspectral image (HSI) [1,2,3,4] contains hundreds of narrow bands, and has been extensively used in different application domains, such as forest monitoring and mapping [5,6], land-use classification [7,8], anomaly detection [9], endmember extraction [10] and environment monitoring [11]
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We have presented a 3D-Gabor inspired Multiview AL (MVAL) framework (i.e., 3D-Gabor-internal and external uncertainty estimation (IEUE)) for spectral-spatial HSI classification
Summary
Hyperspectral image (HSI) [1,2,3,4] contains hundreds of narrow bands, and has been extensively used in different application domains, such as forest monitoring and mapping [5,6], land-use classification [7,8], anomaly detection [9], endmember extraction [10] and environment monitoring [11] Among those kinds of applications, supervised classification is a fundamental task and has been widely studied over the past decades [12,13]. Band selection is studied to reduce the redundancy between contiguous bands [15] and spectral-spatial feature extraction takes advantages of the more distinguishable characteristics [16,17] Among those methods, sufficient labeled samples/pixels are crucial to get the reliable classification results [18]. Since it is difficult to obtain a large number of labeled samples due to the time-consuming and expensive manual labeling process [19], defining a set of high informative training set is one of the solutions
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