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This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud d...
This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal...
Scene matching is the process of locating a region of an image with the corresponding region of another image where both image regions represent the same scene. Although a lot of algorithms have appea...
The number of image features used by content-based remote sensing image retrieval (CBRSIR) system is not less than one hundred, and the image amount is very large, at the same time, the time cost is v...
Soil Desertification has severely threatened inner stability and sustainable development in arid areas, so extracting soil desertification information based on remote sensing and mastering its spatial...
In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imagerie...
从遥感图像提取城市绿地是准确获取城市绿地空间分布的基础。然而由于混合像元的存在,导致城市遥感分类精度不高。因此,利用混合像元分解结合SVM(支持向量机)法提取北京市TM图像城市绿地,并与决策树法比较,研究提高遥感提取城市绿地精度的方法。结果表明,该方法较适合复杂高维空间,对样本选取的准确性没有那么苛刻,可有效地处理城市遥感图像存在的混合像元问题,可较准确地提取城市绿地信息,其精度在92%以上,优于...
在分析支持向量机(Support Vector Machine,SVM)分类技术和机载激光雷达(LIDAR)数据、航空影像特征的基础上,提出了基于SVM的LIDAR数据和航空影像的面向对象建筑物提取方法。结果表明,该方法充分利用了多源影像的互补信息,能够得到更高的信息提取精度,准确而快速地更新地理空间数据库,是一种有效的面向对象建筑物提取方法。

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