期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 9, 页码 7726-7738出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3031928
关键词
Support vector machines; Smoothing methods; Fuses; Imaging; Feature extraction; Minerals; Task analysis; Decision fusion; dual spatial information; feature extraction; hyperspectral classification; structural profile (SP)
类别
资金
- Major Program of the National Natural Science Foundation of China [61890962]
- National Natural Science Foundation of China [61871179]
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- Fund of Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province [2018TP1013]
- Fund of Hunan Province for Science and Technology Plan Project [2017RS3024]
- China Scholarship Council
The research proposes a novel framework for hyperspectral image classification, focusing on extracting spatial information. By fusing dual spatial information, the framework not only accurately extracts discriminative features from HSIs, but also achieves better classification performance through spatial optimization.
The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image (HSI) classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability. This fact has made the extraction of spatial information highly active. In this work, a novel HSI classification framework using the fusion of dual spatial information is proposed, in which the dual spatial information is built by both exploiting pre-processing feature extraction and post-processing spatial optimization. In the feature extraction stage, an adaptive texture smoothing method is proposed to construct the structural profile (SP), which makes it possible to precisely extract discriminative features from HSIs. The SP extraction method is used here for the first time in the remote sensing community. Then, the extracted SP is fed into a spectral classifier. In the spatial optimization stage, a pixel-level classifier is used to obtain the class probability followed by an extended random walker-based spatial optimization technique. Finally, a decision fusion rule is utilized to fuse the class probabilities obtained by the two different stages. Experiments performed on three data sets from different scenes illustrate that the proposed method can outperform other state-of-the-art classification techniques. In addition, the proposed feature extraction method, i.e., SP, can effectively improve the discrimination between different land covers.
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