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Proceedings Paper

Multispectral image enhancement processing for microsat-borne imager
Author(s): Jianying Sun; Zheng Tan; Qunbo Lv; Linlin Pei
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Paper Abstract

With the rapid development of remote sensing imaging technology, the micro satellite, one kind of tiny spacecraft, appears during the past few years. A good many studies contribute to dwarfing satellites for imaging purpose. Generally speaking, micro satellites weigh less than 100 kilograms, even less than 50 kilograms, which are slightly larger or smaller than the common miniature refrigerators. However, the optical system design is hard to be perfect due to the satellite room and weight limitation. In most cases, the unprocessed data captured by the imager on the microsatellite cannot meet the application need. Spatial resolution is the key problem. As for remote sensing applications, the higher spatial resolution of images we gain, the wider fields we can apply them. Consequently, how to utilize super resolution (SR) and image fusion to enhance the quality of imagery deserves studying. Our team, the Key Laboratory of Computational Optical Imaging Technology, Academy Opto-Electronics, is devoted to designing high-performance microsat-borne imagers and high-efficiency image processing algorithms. This paper addresses a multispectral image enhancement framework for space-borne imagery, jointing the pan-sharpening and super resolution techniques to deal with the spatial resolution shortcoming of microsatellites. We test the remote sensing images acquired by CX6-02 satellite and give the SR performance. The experiments illustrate the proposed approach provides high-quality images.

Paper Details

Date Published: 10 October 2017
PDF: 6 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104271W (10 October 2017);
Show Author Affiliations
Jianying Sun, Academy of Opto-Electronics (China)
Zheng Tan, Academy of Opto-Electronics (China)
Qunbo Lv, Academy of Opto-Electronics (China)
Linlin Pei, Academy of Opto-Electronics (China)

Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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