Global land cover knowledge database for supporting optical remote sensing satellite intelligent imaging

Ming Yan; Zhiyong Wang; Shaoshuai He; Fei Wu; Bingyang Yu

doi:10.1117/12.2063693

14 May 2014 Global land cover knowledge database for supporting optical remote sensing satellite intelligent imaging

Ming Yan, Zhiyong Wang, Shaoshuai He, Fei Wu, Bingyang Yu

Proceedings Volume 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China; 91580P (2014) https://doi.org/10.1117/12.2063693
Event: Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 2012, Wuhan, China

Abstract

With the development of high spatial resolution, high spectral resolution, high radiant resolution and high temporal resolution remote sensing satellites being put into use widely, the adaptive intelligent observation becomes an important function of a new generation of satellite remote sensing system. In order to realize the adaptive intelligent observation function, the first step is to construct the land cover priori knowledge and prejudge the land cover types and its reflectance values of the imaging areas. During the satellite imaging, the setting parameters of optimal camera including the on-orbit CCD integral time, electrical gain and image compression ratio are estimated according to the relationship of apparent radiance with sun illumination condition and land surface reflectance. In the paper, Medium Resolution Imaging Spectrometer (MERIS) bimonthly mean land surface reflectance imagery and 2009 GlobCover map are used to build the global land cover and its reflectance knowledge database. The land cover types include the cropland, urban, grassland, forest, desert, soil, water and ice land cover classes and the mean reflectance values in blue, green, red and near infrared spectral band were calculated in various seasons. The global land cover and reflectance values database has been integrated into the Beijing-1 small satellite mission programming system as the priori landscape knowledge of imaging areas to estimate the proper electrical gain of multispectral camera. After the intelligent observation mode was used in Beijing-1 small satellite, the entropy and SNR of multispectral imagery acquired by the Beijing-1 satellite had been increased greatly.

Citation Download Citation

Ming Yan, Zhiyong Wang, Shaoshuai He, Fei Wu, and Bingyang Yu "Global land cover knowledge database for supporting optical remote sensing satellite intelligent imaging", Proc. SPIE 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 91580P (14 May 2014); https://doi.org/10.1117/12.2063693

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KEYWORDS

Reflectivity

Satellite imaging

Satellites

Databases

Remote sensing

Image processing

Near infrared

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