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Perbandingan Klasifikasi Tutupan Lahan Menggunakan Metode Klasifikasi Berbasis Objek dan Klasifikasi Berbasis Piksel pada Citra Resolusi Tinggi dan Menengah

机译:基于对象的分类方法和基于像素的分类在高中分辨率图像上的土地覆盖分类比较

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摘要

A region will experience growth that it will bring changes in the physical appearance. Evolving region need to review land use planning to steer land cover allocation properly. It requires an accurate and effective method to obtain land cover information. One effective technology for mapping land cover is a remote sensing technology. There are various kinds of data processing techniques in remote sensing to obtain land cover information. Classification techniques in remote sensing image are divided into three parts classification technique that are pixel based technique, sub-pixel based technique, and object-based techniques. In this study, the pixel based classification and object based classification techniques will be compared in land cover classification on high resolution imagery that are Quickbird imagery dan medium resolution imagery that are Landsat 8 imagery with the location in city of Semarang. Comparison of the results object based classification and the pixel based classification is tested for accuracy by confusion matrix that produce land cover classification accuracy of Landsat 8 obtained value the overall accuracy for an object based classification method amounted to 77.14%, while the pixel based classification methods obtained a value of 75.71%. For Quickbird image, object based classification produce in overall accuracy of 87.14% while the pixel-based classification obtained a value of 82.85%. The results showing the accuracy of the object based classification is quite good compared to the pixel-based classification either at medium resolution imagery (Landsat 8) and high resolution imagery (Quickbird).
机译:一个区域将经历增长,这将带来物理外观的变化。不断发展的地区需要审查土地使用规划,以正确引导土地覆盖物分配。它需要一种准确有效的方法来获取土地覆被信息。测绘土地覆盖的一种有效技术是遥感技术。遥感中有多种数据处理技术可获取土地覆盖信息。遥感图像的分类技术分为基于像素的技术,基于子像素的技术和基于对象的技术三部分。在这项研究中,将基于像素的分类和基于对象的分类技术在高分辨率图像的土地覆盖分类中进行比较,这些高分辨率图像是Quickbird图像和Landsat 8图像的中分辨率图像,并且位于三宝垄市。通过混淆矩阵测试结果的基于对象的分类与基于像素的分类的比较,以产生Landsat 8的土地覆盖分类精度,获得的值基于对象的分类方法的总体准确性为77.14%,而基于像素的分类方法获得了75.71%的值。对于Quickbird图像,基于对象的分类的整体准确度为87.14%,而基于像素的分类的准确度为82.85%。与基于像素的分类相比,在中分辨率图像(Landsat 8)和高分辨率图像(Quickbird)上,显示基于对象的分类的准确性非常好。

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