【6h】

New applications for mathematical morphology in urban feature extraction from high-resolution satellite imagery

机译:数学形态学在高分辨率卫星影像城市特征提取中的新应用

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摘要

Recently available commercial high-resolution satellite imaging sensors provide an important source for urban remote sensing applications. The high spatial image resolution reveals very fine details in urban areas and greatly facilitates the extraction of urban-related features such as roads, buildings, and vehicles. Since many urban land cover types have significant spectral overlap, structural information obtained using mathematical morphologic operators can provide complementary information to improve discrimination of different urban features. Here we present research demonstrating new applications of mathematical morphology for urban feature extraction from high-resolution satellite imagery. For image preprocessing, an alternating sequential filter is used to eliminate small spatial-scale disturbances to facilitate the extraction of larger-scale structures. For road extraction, directional morphological filtering is exploited to mask out those structures shorter than the distance of a typical city block. For building extraction, a recently introduced concept called the differential morphological profile (DMP) is used to generate building and shadow hypotheses. For vehicle detection, a morphological shared-weight neural network is used to classify image pixels on roads into target and non-target. Thus, mathematical morphology has a wide variety of useful applications for urban feature extraction from high-resolution satellite imagery.
机译:最近可用的商业高分辨率卫星成像传感器为城市遥感应用提供了重要的资源。高空间图像分辨率可显示出非常精细的市区细节,并极大地促进了与城市相关的特征的提取,例如道路,建筑物和车辆。由于许多城市土地覆盖类型具有明显的光谱重叠,因此使用数学形态学算子获得的结构信息可以提供补充信息,以改善对不同城市特征的区分。在这里,我们目前的研究展示了数学形态学在高分辨率卫星图像中提取城市特征的新应用。对于图像预处理,使用交替顺序滤波器来消除较小的空间尺度干扰,以利于提取较大尺度的结构。对于道路提取,利用方向形态过滤来掩盖比典型城市街区的距离短的那些结构。对于建筑物提取,最近引入的称为差分形态学轮廓(DMP)的概念用于生成建筑物和阴影假设。对于车辆检测,使用形态学共享权重神经网络将道路上的图像像素分类为目标和非目标。因此,数学形态学对于从高分辨率卫星图像中提取城市特征具有广泛的有用应用。

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