11 August 2021 Study on ground object classification based on the hyperspectral fusion images of ZY-1(02D) satellite
Jing Yu, Deyin Liang, Bo Han, Hongtao Gao
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Abstract

Spaceborne hyperspectral images are expected to have abundant applications in various fields, particularly in the quantitative observation of the Earth. However, the problem of low spatial resolution has limited their effectiveness to some extent. There are urgent needs for high-spatial-resolution hyperspectral images. We explore fusing the hyperspectral image with panchromatic and multispectral images captured by sensors onboard ZY-1 (02D) to obtain high-spatial-resolution and high-spectral-resolution images. Four approaches to obtain the final high-spatial-resolution hyperspectral image data are proposed, and six fusion methods are used. The fusion results were evaluated using quantitative indicators and classification performance, and image quality greatly improved after fusion. The fusion approach that used multispectral image data as an intermediate layer twice in the fusion process obtained the best fusion effect, and this was verified by both quantitative and application evaluation results. This provides an effective approach to obtain a high-quality, high-spatial-resolution hyperspectral image using the combination of panchromatic, multispectral, and hyperspectral images acquired by the sensors onboard ZY-1(02D).

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2021/$28.00 © 2021 SPIE
Jing Yu, Deyin Liang, Bo Han, and Hongtao Gao "Study on ground object classification based on the hyperspectral fusion images of ZY-1(02D) satellite," Journal of Applied Remote Sensing 15(4), 042603 (11 August 2021). https://doi.org/10.1117/1.JRS.15.042603
Received: 4 May 2021; Accepted: 26 July 2021; Published: 11 August 2021
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CITATIONS
Cited by 10 scholarly publications.
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KEYWORDS
Image fusion

Hyperspectral imaging

Multispectral imaging

Image classification

Satellites

Sensors

Data fusion

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