Functional Feature Extraction for Hyperspectral Image Classification With Adaptive Rational Function Approximation
- 29 January 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 59 (9), 7680-7694
- https://doi.org/10.1109/tgrs.2021.3052807
Abstract
A functional feature extraction method based on rational function approximation for hyperspectral image (HSI) classification is proposed. In digital imagery, the spectral information of a pixel can be regarded as a 1-D signal. An HSI is composed of these 1-D signals arranged in a certain spatial structure. According to the functional characteristic of hyperspectral data, 1-D signals can be approximated by a linear combination of basis functions. Thus, a joint rational basis function system (JRBFS) based on class adaptivity is here first built for an HSI by adaptive Fourier decomposition (AFD). Second, the functional representations (FRs) and corresponding reconstructed spectral curves are obtained by decomposing the original spectral information in a JRBFS. Furthermore, the functional spectral-spatial features are extracted on the basis of FRs by an edge-preserving filtering method, FR-EPFs. Finally, the functional spectral-spatial features are used for HSI classification by SVM. Experimental results for five commonly used HSI data sets demonstrate the effectiveness and advantages of the proposed method FR-EPFs.Keywords
Funding Information
- National Natural Science Foundation of China (62001337, 61877021)
- Natural Science Foundation of Hubei Province (2020CFB136)
- Science and Technology Development Fund of Macao SAR FDCT (079/2016/A2, 0123/2018/A3)
- University of Macau Multi-Year Research (MYRG2018-00111-FST, MYRG2018-00168-FST)
- Fundamental Research Funds for the Central Universities (WUT: 2020IB003)
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