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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution
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Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution

机译：基于自适应差分进化的遥感图像亚像素映射

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摘要

In this paper, a novel subpixel mapping algorithm based on an adaptive differential evolution (DE) algorithm, namely, adaptive-DE subpixel mapping (ADESM), is developed to perform the subpixel mapping task for remote sensing images. Subpixel mapping may provide a fine-resolution map of class labels from coarser spectral unmixing fraction images, with the assumption of spatial dependence. In ADESM, to utilize DE, the subpixel mapping problem is transformed into an optimization problem by maximizing the spatial dependence index. The traditional DE algorithm is an efficient and powerful population-based stochastic global optimizer in continuous optimization problems, but it cannot be applied to the subpixel mapping problem in a discrete search space. In addition, it is not an easy task to properly set control parameters in DE. To avoid these problems, this paper utilizes an adaptive strategy without user-defined parameters, and a reversible-conversion strategy between continuous space and discrete space, to improve the classical DE algorithm. During the process of evolution, they are further improved by enhanced evolution operators, e.g., mutation, crossover, repair, exchange, insertion, and an effective local search to generate new candidate solutions. Experimental results using different types of remote images show that the ADESM algorithm consistently outperforms the previous subpixel mapping algorithms in all the experiments. Based on sensitivity analysis, ADESM, with its self-adaptive control parameter setting, is better than, or at least comparable to, the standard DE algorithm, when considering the accuracy of subpixel mapping, and hence provides an effective new approach to subpixel mapping for remote sensing imagery.
机译：本文提出了一种基于自适应差分进化（DE）算法的亚像素映射算法，即自适应DE亚像素映射（ADESM），以完成遥感图像的亚像素映射任务。在空间依赖性的假设下，子像素映射可以提供来自较粗糙的光谱解混分数图像的类标签的高分辨率图。在ADESM中，为了利用DE，通过最大化空间依赖指数将子像素映射问题转换为优化问题。传统的DE算法在连续优化问题中是一种高效，强大的基于种群的随机全局优化器，但不能应用于离散搜索空间中的子像素映射问题。此外，在DE中正确设置控制参数并非易事。为了避免这些问题，本文采用了无用户定义参数的自适应策略以及连续空间和离散空间之间的可逆转换策略，以改进经典的DE算法。在进化过程中，可以通过增强的进化运算符（例如，突变，交叉，修复，交换，插入和有效的局部搜索以生成新的候选解）进一步改进它们。使用不同类型的远程图像的实验结果表明，在所有实验中，ADESM算法始终优于以前的子像素映射算法。基于灵敏度分析，考虑到子像素映射的准确性，ADESM具有自适应控制参数设置，它优于或至少可与标准DE算法相比，从而为子像素映射提供了一种有效的新方法遥感影像。

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