Land cover mapping at sub-pixel scales using linear optimization techniques
Land cover mapping at sub-pixel scales using linear optimization techniques
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DOI：
10.1016/s0034-4257(01)00242-5
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发表时间：
2002
影响因子：
13.5
通讯作者：
J. Verhoeye;R. Wulf
中科院分区：
文献类型：
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作者：
J. Verhoeye;R. Wulf
当传感器的瞬时视场包括多个地面覆盖类别时，就会产生混合像素。对于混合像素，可以使用模糊分类器，它将一个像素按每个类别覆盖的像素面积的比例分配给几个土地覆盖类别。这些分数值可以分配到子像素，基于空间依赖性的假设和线性优化技术的应用。首先将一种新提出的亚像素映射算法应用于1 km分辨率的合成数据集，该数据集来源于20 m分辨率的图像。该算法生成了500、200和100米分辨率的土地覆盖图，精度接近89%。随后的模式过滤进一步增加了这些值。当应用于实际数据集时，准确率达到78%。虽然这项研究表明了所提出的技术的潜力，但仍有很大的改进和扩展空间。
Mixed pixels result when the sensor's instantaneous field-of-view includes more than one land cover class on the ground. For mixed pixels, fuzzy classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. These fraction values can be assigned to sub-pixels, based on the assumption of spatial dependence and the application of linear optimization techniques. A newly proposed sub-pixel mapping algorithm was first applied to a synthetic data set with a 1-km resolution, derived from a 20-m resolution image. This algorithm yielded land cover maps at 500, 200, and 100 m resolution with accuracies close to 89%. Subsequent mode filtering further increased these values. When applied to a real data set, the accuracy reached 78%. While this study suggests the potential of the proposed technique, there is still ample scope for improvements and extensions.