首页> 外文会议>Proceedings of the 22nd Asian Conference on Remote Sensing >DETECTING FRAGMENTED MANGROVES IN THE SUNDARBANS, BANGLADESH USING OPTICAL AND RADAR SATELLITE IMAGES
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DETECTING FRAGMENTED MANGROVES IN THE SUNDARBANS, BANGLADESH USING OPTICAL AND RADAR SATELLITE IMAGES

机译:使用光学和雷达卫星图像检测孟加拉邦桑德班斯中的碎红鱼

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S: This study shows that Landsat TM, JERS-VNIR (optical) and JERS-SAR (radar) data can be used for detecting edges of fragmented mangrove in the Sundarbans, Bangladesh. The classification JERS-SAR image gives highest overall classification accuracy of 85.53% and per class accuracy of 86.49% in detecting high contrast edges. Whereas overall classification accuracy of Landsat TM and JERS-VNIR image are 80.52% and 82.27% respectively. Mapping of high contrast edges can be done through producing segment map from on screen digitising of class high contrast edges on classification map of all three sensors mentioned above. It is believed that the high contrast edges, with their specific spatial characteristics and pattern (e.g., linear feature shape and network like pattern) can be detected spatially on remotely sensed data.
机译:S:这项研究表明,Landsat TM,JERS-VNIR(光学)和JERS-SAR(雷达)数据可用于检测孟加拉国Sundarbans中破碎的红树林边缘。在检测高对比度边缘时,分类JERS-SAR图像具有最高的总体分类准确度,达到85.53%,每类准确度为86.49%。 Landsat TM和JERS-VNIR图像的总体分类准确度分别为80.52%和82.27%。高对比度边缘的映射可以通过在上面提到的所有三个传感器的分类图上将高对比度边缘类的屏幕数字化而生成分段图来完成。可以相信,具有高对比度的边缘及其特定的空间特征和图案(例如,线性特征形状和类似网络的图案)可以在遥感数据上进行空间检测。

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