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Open AccessArticle

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

by Jie Hu 1, Zhi He 1,*, Jun Li 1,*, Lin He 2 and Yiwen Wang 1
1
Guangdong Province Key Laboratory of Urbanization and Geo-Simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1070; https://doi.org/10.3390/rs10071070
Received: 16 May 2018 / Revised: 27 June 2018 / Accepted: 3 July 2018 / Published: 5 July 2018
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: hyperspectral image classification; multiview active learning (MVAL); 3D-Gabor; feature assessment; sampling selection hyperspectral image classification; multiview active learning (MVAL); 3D-Gabor; feature assessment; sampling selection
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MDPI and ACS Style

Hu, J.; He, Z.; Li, J.; He, L.; Wang, Y. 3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification. Remote Sens. 2018, 10, 1070.

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