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Terrain-cover classification by integration of SPOT and ERS-1 SAR images over Taiwan

机译:地形覆盖分类通过派分和eRS-1 SAR图像整合到台湾

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This paper is aimed at the integration of multispectral high resolution visible SPOT image and ERS-1 C-band SAR image data for classification of terrain-cover. The test site was located at central west of Taiwan, where seven terrain covers, from water body to vegetation, were identified. As the first part of this study, the authors apply a simple model to compensate for the elevation effect in the ERS-1 SAR image using the existing DTM data. The geometrically rectified SAR image is then overlaid with the orthoimage of SPOT. The rms error of this overlay produces approximately one pixel or about 12 m which is acceptable for this study. The second part of the study deals with the land cover classification. In particular, fractal image extracted from SAR data is added. So both spectral and spatial information are used simultaneously for classification. Supervised classification was followed to discriminate the terrain-cover from combined images. Extensive ground truth collection along with available base map were used to aid the evaluation of classification accuracy. It was found that when combined them together better classification accuracy can be obtained.
机译:本文的目的是在积分多谱段分辨率高可见SPOT影像和ERS-1 C波段SAR图像数据的地形覆盖分类。测试场地位于台湾,其中7个地形盖,从水体植被,被确定的中西部。作为这项研究的第一部分中,作者运用一个简单的模型来补偿使用现有的DTM数据的ERS-1合成孔径雷达图像中的高程效应。几何纠正SAR图像,然后用点的正射影像覆盖。此叠加的RMS误差约产生一个像素或约12m这对于本研究是可接受的。该研究的第二部分与土地覆盖分类交易。特别是,从SAR数据中提取的分形图像被添加。这样的光谱和空间信息被同时用于分类。监督分类之后,从组合图像判别地形盖。与现有底图一起广泛地收集事实被用来帮助分类准确度的评价。结果发现,当他们一起结合可以得到更好的分类准确度。

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