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Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification

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Abstract

The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications. As two kinds of typical optical remote sensing data, multispectral images (MSIs) and hyperspectral images (HSIs) have complementary characteristics. The MSI has a large swath and short revisit period, but the number of bands is limited with low spectral resolution, leading to weak separability of between class spectra. Compared with MSI, HSI has hundreds of bands and each of them is narrow in bandwidth, which enable it to have the ability of fine classification, but too long in aspects of revisit period. To make efficient use of their combined advantages, multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote sensing. To deal with the collaborative classification, most of current methods are unsupervised and only consider the HSI reconstruction as the objective. In this paper, a class-guided coupled dictionary learning method is proposed, which is obviously distinguished from the current methods. Specifically, the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms, so as to enforce the learned coupled dictionaries to be both representational and discriminative. The learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients, while pixels from different categories have different sparse represent coefficients. The experiments on three pairs of HSI and MSI have shown better classification performance.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China

    TianZhu Liu & YanFeng Gu

  2. Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing, Harbin, 150001, China

    TianZhu Liu & YanFeng Gu

  3. School of Engineering and Information Technology, University of New South Wales, Canberra, 2610, Australia

    XiuPing Jia

Authors
  1. TianZhu Liu
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  2. YanFeng Gu
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  3. XiuPing Jia
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Corresponding author

Correspondence to YanFeng Gu.

Additional information

This work was supported by the National Natural Youth Science Foundation Project (Grant No. 62001142), the Key International Cooperation Project (Grant No. 61720106002), the Distinguished Young Scholars of National Natural Science Foundation of China (Grant No. 62025107), and Heilongjiang Postdoctoral Fund (Grant No. LBH-Z20068).

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Liu, T., Gu, Y. & Jia, X. Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification. Sci. China Technol. Sci. 65, 744–758 (2022). https://doi.org/10.1007/s11431-021-1978-6

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  • DOI: https://doi.org/10.1007/s11431-021-1978-6

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