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基于概率扩散的多光谱遥感图像分类模型 被引量:1
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作者 王毅 张良培 李平湘 《中国图象图形学报》 CSCD 北大核心 2006年第5期646-651,I0001,共7页
为了提高遥感图像分类精度,提出了一种基于概率扩散模型的多光谱遥感图像自动分类技术。该方法首先通过比较模糊C均值分类器(FCM)的有效性函数来自动确定最优分类数目,然后利用基于形态学的各向异性概率扩散模型来调整中心像元隶属类别... 为了提高遥感图像分类精度,提出了一种基于概率扩散模型的多光谱遥感图像自动分类技术。该方法首先通过比较模糊C均值分类器(FCM)的有效性函数来自动确定最优分类数目,然后利用基于形态学的各向异性概率扩散模型来调整中心像元隶属类别的概率,最后根据概率扩散的隶属概率向量图,并按照最大后验概率估计(MAP)对像元进行分类。由于各向异性扩散具有保边缘平滑的特点,因此,该概率扩散模型不仅能够有效地抑制同质区域内部“斑点”的产生,而且使得图像上重要的边缘特征得到了较好地保留。实验结果表明,该分类算法不仅能够避免分类图像中“斑点”噪声的影响,而且分类后的总体精度达到了77.76%和Kappa系数达到了0.7198,均优于未经过概率扩散的最大后验概率估计分类算法,因而具有一定的实用价值。 展开更多
关键词 各向异性扩散 概率扩散 多光谱遥感图像分类 最大后验概率估计 扩散系数
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High-resolution remote sensing mapping of global land water 被引量:26
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作者 LIAO AnPing CHEN LiJun +6 位作者 CHEN Jun HE ChaoYing CAO Xin CHEN Jin PENG Shu SUN FangDi GONG Peng 《Science China Earth Sciences》 SCIE EI CAS 2014年第10期2305-2316,共12页
Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article ... Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China's HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment. 展开更多
关键词 global land cover land surface water 30 m resolution classification method remote sensing mapping
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