URBAN SHANTY TOWN RECOGNITION BASED ON HIGH-RESOLUTION REMOTE SENSING IMAGES AND NATIONAL GEOGRAPHICAL MONITORING FEATURES – A CASE STUDY OF NANNING CITY

He Y.

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URBAN SHANTY TOWN RECOGNITION BASED ON HIGH-RESOLUTION REMOTE SENSING IMAGES AND NATIONAL GEOGRAPHICAL MONITORING FEATURES – A CASE STUDY OF NANNING CITY

机译：基于高分辨率遥感影像和国家地理监测特征的城市棚户区识别-以南宁市为例

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Urban shanty towns are communities that has contiguous old and dilapidated houses with more than 2000 square meters built-up area or more than 50 households. This study makes attempts to extract shanty towns in Nanning City using the product of Census and TripleSat satellite images. With 0.8-meter high-resolution remote sensing images, five texture characteristics (energy, contrast, maximum probability, and inverse difference moment) of shanty towns are trained and analyzed through GLCM. In this study, samples of shanty town are well classified with 98.2?% producer accuracy of unsupervised classification and 73.2?% supervised classification correctness. Low-rise and mid-rise residential blocks in Nanning City are classified into 4 different types by using k-means clustering and nearest neighbour classification respectively. This study initially establish texture feature descriptions of different types of residential areas, especially low-rise and mid-rise buildings, which would help city administrator evaluate residential blocks and reconstruction shanty towns.

机译：城市棚户区是指拥有破旧的旧房屋的社区，这些房屋的建筑面积超过2000平方米，或有50多个家庭。这项研究尝试使用人口普查和TripleSat卫星图像的产品来提取南宁市的棚户区。利用0.8米的高分辨率遥感图像，通过GLCM对棚户区的五个纹理特征（能量，对比度，最大概率和反差矩）进行了训练和分析。在这项研究中，棚户区的样本被很好地分类，无监督分类的生产者准确性为98.2％，监督分类的正确性为73.2％。通过k均值聚类和最近邻分类，将南宁市的低层和中层住宅区分为4种类型。这项研究最初建立了不同类型住宅区的纹理特征描述，特别是低层和中层建筑，这将有助于城市管理者评估住宅区和重建棚户区。

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《International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences》 |2018年第2期|共6页
作者
He Y.;
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中图分类摄影测量学与测绘遥感;
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Gray-level co-occurrence matrixshanty townresidential suitabilitynearest neighbour;

机译：灰度共现矩阵棚户区居民适用性最近邻;

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URBAN SHANTY TOWN RECOGNITION BASED ON HIGH-RESOLUTION REMOTE SENSING IMAGES AND NATIONAL GEOGRAPHICAL MONITORING FEATURES – A CASE STUDY OF NANNING CITY

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