Characteristics of multi-exposure images of BJ-1 intelligent micro satellite and its applications to snow cover extraction
YAN Fuli1, XU Jianguo2, LU Zhihong3
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
2. No. 216 Party of China National Nuclear Corporation, Urumqi 830011, China;
3. Agaricultural Bureau of Qixia, Qixia 265300, China
BJ-1 micro satellite can adjust CCD exposure by altering the imaging integration time, which not only can affect the digital numbers(DN)and the image quality but also can bring about the uncertainties in retrieving the land surface parameters quantitatively. Taking the HJ-1 CCD datasets as references, the authors calculated and analyzed the spectral characteristics and image quality of the high reflective targets in BJ-1 CCD images. Some conclusions have been reached:The image quality will be improved with a moderate exposure and impaired with over-exposure. The spectral features of the snow in terrain shadow can be enhanced with the increasing imaging integration time, while the spectral characteristics of the snow on the sun side would be weakened as a result of the image "saturation". On the basis of the spectral features of the snow and the data characteristics of the multi-exposure BJ-1 CCD imageries, a normalized model of the multi-exposure imageries for extracting the snow cover is proposed with the accuracy (classification success index, CSI)of 89.95%, which is superior to the accuracy (CSI)of 82.25% of mono-exposure model and the accuracy(CSI) of 75.95% of supervised classification. The results achieved by the authors will greatly facilitate the design of the more target-specific intelligent sensor and the development of more accurate inversion theory and techniques in retrieving the land surface parameters quantitatively.
阎福礼, 徐建国, 鲁志弘. BJ-1智能小卫星多曝光量数据特征及其积雪提取方法研究[J]. 国土资源遥感, 2016, 28(1): 28-34.
YAN Fuli, XU Jianguo, LU Zhihong. Characteristics of multi-exposure images of BJ-1 intelligent micro satellite and its applications to snow cover extraction. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 28-34.
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2016, Vol. 28 
