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Enhanced Subpixel Mapping With Spatial Distribution Patterns of Geographical Objects

机译：具有地理对象空间分布模式的增强型亚像素映射

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This paper proposes spatial distribution pattern-based subpixel mapping (SPM) as a novel subpixel mapping (SPM) strategy. It separately considers spatial distribution patterns of different types of geographical objects. Initially, it classifies geographical objects into areal, linear, and point patterns according to their spatially geometric characteristics. For the different patterns, SPM uses the vectorial boundary-based SPM algorithm with the spatial dependence assumption to deal with areal objects, the linear template matching-based SPM algorithm for linear objects, and the spatial pattern consistency matching-based SPM algorithm for point objects. The three patterns are integrated to generate a subpixel map. An artificially created image and two remotely sensed images were used to evaluate the performance of SPM. The results were compared with a traditional hard classifier and seven existing SPM methods. The experimental results demonstrated that SPM performed better than the hard classification and traditional SPM methods, particularly when dealing with linear and point objects.
机译：本文提出了一种基于空间分布模式的子像素映射（SPM）作为一种新颖的子像素映射（SPM）策略。它分别考虑了不同类型地理对象的空间分布模式。最初，它根据地理对象的空间几何特征将其分为平面，线性和点模式。对于不同的模式，SPM使用基于矢量边界的SPM算法（具有空间依赖性假设）来处理面对象，针对线性对象的基于线性模板匹配的SPM算法以及针对点对象的基于空间模式一致性匹配的SPM算法。集成这三个图案以生成子像素图。人工创建的图像和两个遥感图像用于评估SPM的性能。将结果与传统的硬分类器和七个现有的SPM方法进行了比较。实验结果表明，SPM的性能优于硬分类和传统的SPM方法，尤其是在处理线性和点对象时。

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