Landsat TM 遥感影像中厚云和阴影如何去除

1007-4619 (2010) 03-534-12Journal of Remote Sensing遥感学报Cloud and shadow removal from Landsat TM dataRI Pyongsop1, 2, MA Zhangbao1, QI Qingwen1, LIU Gaohuan11. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 2. Institute of Remote Sensing and Geo-informatics, Pyongyang, Democratic People’s Republic of KoreaAbstract: Cloud removal is an important step in remote sensing image process. In this paper, the author proposed a new algorithm for cloud removal using multi-temporal Landsat TM image data based on spectral characteristics analysis. Through the spectral characteristics analysis of the thick cloud region and its shadow region, the thick cloud and its shadow identification models were designed. Using image regression, unsupervised classification and pixel replacing techniques as well as these models, the influence of thick clouds and its shadows can be eliminated or reduced in the Landsat TM images. The result shows that the algorithm can eliminate or significantly reduce the cloud influence from Landsat TM image data. Key words: Landsat TM, image data, cloud and shadow, spectral analysis, cloud removal CLC number: TP751.1 Document code: ACitation format: RI Pyongsop, Ma Z B, Qi Q W and Liu G H. 2010. Cloud and shadow removal from LANDSAT TM data. Journal of Remote Sensing. 14(3): 534—5451INTRODUCTIONThe earth observing satellite Landsat TM/ETM+ remote sensing image data, have been widely used as the main data source for the study of spatial/ temporal land use/cover change due to its enhanced spectral characteristics, short data acquisition cycle, wide survey field, data usability and other properties (Li et al., 1997). It has been used as an ideal remote sensing image data source for the research of regional-scale natural resource and environment. However, due to climate reasons, it is difficult to obtain completely cloud free remote sensing image data. Most of the remote sensing image data include, more or less, clouds and their shadows projected on the ground. These give some trouble to a number of users of remote sensing image data. It becomes often the most important issue how to remove the influence of clouds from the remote sensing image data (Song et al., 2006). So, cloud removal is an essential step in the image pre-processing process (Song et al., 2003). A great number of work has been carried out on the cloud detection and removal research such as dynamic filtering method (Zhao, 1996; Wu, 2003), multi-spectral synthesis method, light temperature value difference method, the index method (Song et al., 2003), cloud processing algorithms based on remote sensing image classification results and the cloud detection results (Song et al., 2006), image fusion method based on neural network and wavelet transform (Tapasmini et al., 2008), cloud detection method based on the texture analysis and neural network (Song et al., 2004).The conventional c