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Segmenting and extracting terrain surface signatures from fully polarimetric multilook SIR-C data

机译:从完全偏振的MultileRic SiR-C数据分段和提取地形表面签名

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We report results from the segmenting and study of terrain surface signatures of fully polarimetric multilook L-band and C-band SIR-C data. Entropy/alpha/anisotropy decomposition features are available from single multilook pixel data. This eliminates the need to average data from several pixels. Entropy and alpha are utilized in the segmentation along with features we have developed primarily from the eigenanalysis of the Kennaugh matrices of multilook data. We have previously reported on our algorithm for segmenting fully polarimetric single look TerraSAR-X, multilook SIR-C and 7 band Landsat 5 data featuring the iterative application of a feedforward neural network with one hidden layer. A comparison of signatures from simultaneously recorded data at L and C bands is presented. The terrain surfaces surveyed include the ocean, lakes, lake ice, bare ground, desert salt flats, lava beds, vegetation, sand dunes, rough desert surfaces, agricultural and urban areas.
机译:我们报告了完全偏振的多圆形L波段和C波段SIR-C数据的地形表面签名的分段和研究结果。熵/ alpha /各向同性分解特征可从单个Multook像素数据提供。这消除了来自几个像素的平均数据的需要。熵和alpha在分段中使用,以及我们主要从Multilook数据的肯普矩阵的特征分析开发的特征。我们之前报道了我们的分割全极偏振单个外观Terrasar-X,MultiLook SiR-C和7频段LANDSAT 5数据,其中包含一个隐藏层的前馈神经网络的迭代应用。呈现了L和C频段同时记录数据的签名的比较。接受调查的地形表面包括海洋,湖泊,湖冰,裸地,沙漠盐平,熔岩床,植被,沙丘,粗糙的沙漠表面,农业和城市地区。

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