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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm
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Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm

机译：基于实例回归的超高分辨率土地覆盖制图算法的改进

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摘要

Super-resolution mapping (SRM) is a method for generating a fine-resolution land cover map from coarse-resolution fraction images. Example-regression-based SRM algorithms can estimate a fine-resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated fine-resolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.
机译：超分辨率映射（SRM）是一种从粗糙分辨率的分数图像生成高分辨率的土地覆盖图的方法。基于示例回归的SRM算法可以通过从可用的土地覆盖图中学习土地覆盖空间模式，来估计具有详细空间信息的高分辨率土地覆盖图。现有的基于示例回归的SRM算法对分数误差很敏感，其结果通常包含许多线性伪影和斑点。为了克服这些缺点，本研究提出了一种改进的基于示例回归的SRM算法。所提出的SRM算法的目标函数包括三个项。第一项用于最小化估算的高分辨率土地覆盖图的分数值与输入分数值之间的差异。第二项用于最大化结果中精细像素的类成员资格可能性值。最后一项用于使结果局部平滑。使用合成图像和实数分数图像，将提出的SRM算法与几种流行的SRM算法进行了比较。实验结果表明，与用于比较的SRM结果相比，所提出的SRM算法所产生的结果具有更少的斑点和线性伪影，更多的空间细节，更平滑的边界以及更高的精度。

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