%0 Journal Article
%T Ship detection from low observable regions in optical remote sensing imagery<br>光学遥感图像低可观测区域舰船检测
%A Zhou Wei
%A Guan Jian
%A He You
%A <br>周伟
%A 关键
%A 何友
%J 中国图象图形学报
%D 2012
%I 
%X Local cloud and fog can cause low contrast and poor visibility in optical remote sensing images of certain ocean regions, which hinders ship surveillance. To overcome this, a multi-scale phase spectrum is used to reconstruct the low observable image to form a saliency map in the first step. Then, a global threshold is used to extract the regions of interest (ROI), which has higher saliency. The order statistic of mean intensities from the sub regions of a circular window around each ROI is used to estimate the local threshold for target pixel segmentation. The mean saliency, shape complexity, and spatial extent are extracted from the target pixels to form a feature vector. Then a minimum distance classifier on the extracted feature vector is trained to discard the false alarms. Results on many cloudy SPOT-4 panchromatic images show the effectiveness of the proposed algorithm.
%K ship detection
%K saliency map
%K optical remote sensing
%K feature extraction
%K minimum distance classifier<br>舰船检测
%K 显著图
%K 光学遥感
%K 特征提取
%K 最小距离分类器
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0B63987600F2A39C9A2C12B46DD64599&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=9CF7A0430CBB2DFD&sid=7B747CE18E2C7596&eid=F61A98B4CFAD5F2A&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=16