%0 Journal Article
%T Sub-pixel mapping method based on ANN and super-resolution reconstructed model<br>结合超分辨率重建的神经网络亚像元定位方法
%A WU Ke
%A NIU Ruiqing
%A SHEN Huanfeng
%A LING Feng
%A CHEN Tao
%A <br>吴柯
%A 牛瑞卿
%A 沈焕峰
%A 凌峰
%A 陈涛
%J 中国图象图形学报
%D 2010
%I 
%X Mixed pixels are always the case in remote sensed images, and how to analysis and explain mixed pixels is of importance in remote sensing applications. Sub-pixel mapping is a technique designed to obtain the spatial distribution of the classes inside the pixels with information of different endmembers to improve the accuracy of the classification. In this paper, a new BPMAP model is introduced by combination of the neural network and super-resolution reconstructed technology. The spatial distribution of the sub-pixel can be determined by establishing of observation model between the high-resolution and the low-resolution images after the neural network mapping; with restricted by Maximum A Posteriori (MAP) algorithm. The proposed model was tested on both simple synthetic image and ETM image in the three Gorges area. Results indicate that this method can mapping sub-pixel efficiently, and better performance was observed compared to that of the original ANN model.
%K mixed pixels
%K super-resolution
%K BPNN model
%K MAP
%K observation model<br>混合像元
%K 超分辨率
%K BP神经网络模型
%K 最大后验估计方法
%K 观测模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=720F0FFF286F6EE16C82EFA3E7B7B140&yid=140ECF96957D60B2&vid=23CCDDCD68FFCC2F&iid=708DD6B15D2464E8&sid=B219870B99929345&eid=5A16362B1718D389&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=1&reference_num=0