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首页> 外文会议>Joint Urban Remote Sensing Event >Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka
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Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka

机译：评估从高分辨率空间卫星数据得出的空间和光谱特征与斯里兰卡科伦坡的城市贫困之间的关系

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In this study we seek to map urban poverty in Colombo, Sri Lanka using spectral and spatial features estimated from high spatial resolution satellite imagery. For this study we calculated 165 spectral and spatial features at a block size of 16m and a range of scales, from three Quickbird scenes, collected in 2010 which cover 316 Grama Niladhari (GN) census units within the District of Colombo and includes the urban area of Colombo, Sri Lanka. The features calculated include linear support regions (LSR), linear binary pattern moments (LBPM), PanTex, Histogram of Oriented Gradients (HoG), Speeded Up Robust Features (SURF), Fourier Transform (FT), Gabor, the mean of each of the blue, green, red, and near-infrared spectral bands, as well as the Normalized Difference Vegetation Index (NDVI). For each GN census unit (avg. size of 2.17 sq. km), the zonal sum, mean, and standard deviation of all 165 features were calculated. For each GN unit, the 10/20/30/40th percentiles of the national distribution of household estimates of predicted per capita consumption were calculated using data from the 2011 Sri Lankan Census to provide an estimate of poverty. Results indicate that the combined spatial and spectral features were able to explain up to 54% of the variation in poverty when using a simple, ordinary least squares linear regression model.
机译：在这项研究中，我们试图使用从高空间分辨率卫星图像估计的光谱和空间特征来绘制斯里兰卡科伦坡的城市贫困图。在本研究中，我们从2010年收集的三个Quickbird场景中计算了165个光谱和空间特征，块大小为16m，范围为一系列尺度，这些场景涵盖了科伦坡地区的316个Grama Niladhari（GN）人口普查单位，包括市区斯里兰卡科伦坡市。计算出的特征包括线性支撑区域（LSR），线性二进制模式矩（LBPM），PanTex，定向梯度直方图（HoG），加速鲁棒特征（SURF），傅立叶变换（FT），Gabor，每一个的均值蓝色，绿色，红色和近红外光谱带以及归一化差异植被指数（NDVI）。对于每个GN人口普查单位（平均面积2.17平方公里），计算了全部165个特征的纬度总和，均值和标准差。对于每个GN单位，均使用2011年斯里兰卡人口普查数据计算了全国家庭人均预测消费量估计数的10/20/30/40％，以提供贫困估计数。结果表明，当使用简单的普通最小二乘线性回归模型时，组合的空间和光谱特征能够解释多达54％的贫困变化。

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