Direct sub-pixel mapping exploiting spatial dependence
Direct sub-pixel mapping exploiting spatial dependence
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DOI：
10.1109/igarss.2004.1370340
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发表时间：
2004-12
期刊：
影响因子：
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通讯作者：
K. Mertens;B. Baets;L. Verbeke;R. Wulf
中科院分区：
文献类型：
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作者：
K. Mertens;B. Baets;L. Verbeke;R. Wulf
遥感图像通常包含纯像素和混合像素。清晰的分类技术将混合像素分配给覆盖率或概率最高的类别。不幸的是，在此过程中信息会丢失。引入软或模糊分类技术来弥补这种损失，通过将分数分配给与像素内表示的区域相对应的土地覆盖类别。模糊分类产生的分数图像数量等于分类中考虑的土地覆盖类别的数量。然而，对这些类的分配不会呈现有关这些分数在像素内的位置的信息。下午。 Atkinson (1997) 指出，可以将这些分数在空间上分配给所谓的“子像素”。子像素是从父像素导出的更精细的表示。这项工作介绍了一种利用空间依赖性的子像素映射算法。周围像素中不同类别分数的空间排列用于查找中心像素内的子像素的位置。子像素映射算法旨在应用于高空间分辨率的分数图像。通过使用通过将硬分类降级为较粗糙的空间分辨率而创建的合成图像，可以排除由于配准和不良分类而导致的错误。生成的图像被解释为分数图像。然后测试算法重建原始硬分类的能力。使用标准分类精度测量来评估算法的精度。所提出的算法以简单的方式结合了空间依赖性，在有限的计算时间内达到准确的结果
Remotely sensed images usually contain both pure and mixed pixels. Crisp classification techniques assign mixed pixels to the class with the highest proportion of coverage or probability. Unfortunately, during this process information is lost. Soft or fuzzy classification techniques were introduced to make up for this loss by assigning fractions to the land cover classes in correspondence with the area represented inside a pixel. A fuzzy classification yields a number of fraction images equal to the number of land cover classes considered in the classification. However, the assignment to these classes renders no information about the location of these fractions inside the pixel. P.M. Atkinson (1997) stated that it is possible to assign the fractions spatially to so called 'sub-pixels'. Sub-pixels are a finer representation derived from a parent pixel. This work introduces a sub-pixel mapping algorithm exploiting spatial dependence. The spatial arrangement of the different class fractions in surrounding pixels is used to find the location of the sub-pixels inside the central pixel. The sub-pixel mapping algorithm is intended to he applied to fraction images of a high spatial resolution. Errors due to coregistration and poor classification are excluded by using synthetic imagery that was created by degrading hard classifications to coarser spatial resolutions. Resulting images are interpreted as fraction images. The algorithm is then tested for its ability to reconstruct the original hard classification. The accuracy of the algorithm is evaluated using standard classification accuracy measures. The algorithm proposed incorporates spatial dependence in a simple manner, reaching accurate results in a limited computation time