Wang Li-Tao, Zhou Yi, Zhou Qiang, Wang Shi-Xin, Yan Fu-Li
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Aug;31(8):2226-32.
Environment and Disasters Monitoring Microsatellite Constellation with high spatial resolution, high temporal resolution and high spectral resolution characteristics was put forward by China. HJ-1B satellite, one of the first two small optical satellites, had a CCD camera and an infrared camera, which would provide an important new data source for snow monitoring. In the present paper, through analyzing the sensor and data characteristics of HJ-1B, we proposed a new infrared normalized difference snow index (INDSI) referring to the traditional normalized difference snow index (NDSI). The accuracy of these two automatic snow recognition methods was estimated based on a supervised classification method. The accuracy of the traditional NDSI method was 97.761 9% while that of the new INDSI method was 98.617 1%.
环境与灾害监测小卫星星座是中国提出的具有高空间分辨率、高时间分辨率和高光谱分辨率特征的卫星星座。HJ-1B卫星是首批两颗小型光学卫星之一，搭载了一台电荷耦合器件（CCD）相机和一台红外相机，将为积雪监测提供重要的新数据源。本文通过分析HJ-1B卫星的传感器及数据特征，参考传统归一化差异积雪指数（NDSI），提出了一种新的红外归一化差异积雪指数（INDSI）。基于监督分类方法对这两种自动积雪识别方法的精度进行了评估。传统NDSI方法的精度为97.761 9%，而新的INDSI方法的精度为98.617 1%。
