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Multimodal hyperspectral remote sensing: an overview and perspective

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Abstract

Since the advent of hyperspectral remote sensing in the 1980s, it has made important achievements in aerospace and aviation field and been applied in many fields. Conventional hyperspectral imaging spectrometer extends the number of spectral bands to dozens or hundreds, and provides spatial distribution of the reflected solar radiation from the scene of observation at the same time. Nowadays, with the fast development of new technology in the fields of information and photoelectricity sensing, and the popularity of unmanned aerial vehicle, hyperspectral remote sensing imaging presents the new trends of multimodality and acquires integration information while keeping high or very-high spectral resolution, especially, high temporal even real time sensing and stereo sensing. Therefore, three important modes of hyperspectral imaging come into existence: (1) multitemporal hyperspectral imaging, which refers to the observation of same region at different dates; (2) hyperspectral video imaging, which captures full frame spectral images in real-time; (3) hyperspectral stereo imaging, which obtains the full dimension information (including 2D image, elevation, and spectra) of observed scene. Along this perspective, firstly, the current researches on hyperspectral remote sensing and image processing are briefly reviewed, and then, comprehensive descriptions of the aforementioned three main hyperspectral imaging modes are carried out from the following four aspects: fundamental principle of new mode of hyperspectral imaging, corresponding scientific data acquisition, data processing and application, and potential challenges in data representation, feature learning and interpretation. Through the analysis of development trend of hyperspectral imaging and current research situation, we hope to provide a direction for future research on multimodal hyperspectral remote sensing.

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Acknowledgements

This work was supported by National Natural Science Foundation of Key International Cooperation of China (Grant No. 61720106002) and National Key R&D Program of China (Grant No. 2017YFC1405100). The authors would like to thank Beijing Anzhou Technology Co. LTD for providing the HSV data shown in Figure 7.

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Authors and Affiliations

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

    Yanfeng Gu, Tianzhu Liu & Guoming Gao

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

    Yanfeng Gu, Tianzhu Liu & Guoming Gao

  3. First Institute of Oceangraphy, Ministry of Natural Resources, Qingdao, 266061, China

    Guangbo Ren & Yi Ma

  4. GIPSA-Lab, Grenoble Institute of Technology, Grenoble, 38402, France

    Jocelyn Chanussot

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

    Xiuping Jia

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  1. Yanfeng Gu
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Gu, Y., Liu, T., Gao, G. et al. Multimodal hyperspectral remote sensing: an overview and perspective. Sci. China Inf. Sci. 64, 121301 (2021). https://doi.org/10.1007/s11432-020-3084-1

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