Multiple Kernel Learning for Hyperspectral Image Classification: A Review

With the rapid development of spectral imaging techniques, classification of hyperspectral images (HSIs) has attracted great attention in various applications such as land survey and resource monitoring in the field of remote sensing. A key challenge in HSI classification is how to explore effective...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2017-11, Vol.55 (11), p.6547-6565
Main Authors: Yanfeng Gu, Chanussot, Jocelyn, Xiuping Jia, Benediktsson, Jon Atli
Format: Article
Language:English
Subjects:
Algorithms
Capacity
Classification
Data structures
Engineering Sciences
heterogeneous features
hyperspectral images (HSIs)
Hyperspectral imaging
Image classification
Imaging techniques
Kernel
Kernels
Land surveys
multiple kernel learning (MKL)
Neural networks
Remote monitoring
Remote sensing
Signal and Image processing
Spectra
Support vector machines
Surveying
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Summary:With the rapid development of spectral imaging techniques, classification of hyperspectral images (HSIs) has attracted great attention in various applications such as land survey and resource monitoring in the field of remote sensing. A key challenge in HSI classification is how to explore effective approaches to fully use the spatial-spectral information provided by the data cube. Multiple kernel learning (MKL) has been successfully applied to HSI classification due to its capacity to handle heterogeneous fusion of both spectral and spatial features. This approach can generate an adaptive kernel as an optimally weighted sum of a few fixed kernels to model a nonlinear data structure. In this way, the difficulty of kernel selection and the limitation of a fixed kernel can be alleviated. Various MKL algorithms have been developed in recent years, such as the general MKL, the subspace MKL, the nonlinear MKL, the sparse MKL, and the ensemble MKL. The goal of this paper is to provide a systematic review of MKL methods, which have been applied to HSI classification. We also analyze and evaluate different MKL algorithms and their respective characteristics in different cases of HSI classification cases. Finally, we discuss the future direction and trends of research in this area.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2017.2729882