期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 55, 期 1, 页码 119-133出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2598065
关键词
Classification; curvelet transform; hyperspectral imaging (HSI); singular spectrum analysis (SSA); spatial post-processing (SPP); support vector machine (SVM)
类别
资金
- National Natural Science Foundation of China [61672008, 61202165, 61572351]
- Science and Technology Major Project of Education Department of Guangdong Province, China [2014KZDXM060]
- Natural Science Foundation of Guangdong Province, China [2016A030311013]
- International Scientific and Technological Cooperation Projects of Education Department of Guangdong Province, China [2015KGJHZ021]
Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet-transformed domain via a relatively new spectral feature processing technique-singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracy over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artifacts introduced during the data acquisition process. By adding an extra spatial postprocessing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods.
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