Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution
Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution
复制标题
基于自适应差分进化的遥感图像亚像素映射
DOI：
10.1109/tsmcb.2012.2189561
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发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Zhong, Yanfei;Zhang, Liangpei
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adaptive-DE亚像素映射（ADESM），用于完成遥感图像的亚像素映射任务。亚像素映射可以在空间依赖的假设下，从较粗的光谱分解分数图像中提供精细分辨率的类标签图。在ADESM中，为了利用DE，将亚像素映射问题转化为空间依赖指数最大化的优化问题。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，但它不适用于离散搜索空间中的亚像素映射问题。此外，在DE算法中，控制参数的合理设置也不是一件容易的事情。为了避免这些问题，本文采用了不自定义参数的自适应策略，以及连续空间与离散空间之间的可逆转换策略，对经典DE算法进行了改进。在进化过程中，通过增强的进化算子，如突变、交叉、修复、交换、插入，以及有效的局部搜索来产生新的候选解，进一步改进了它们。不同类型遥感图像的实验结果表明，ADESM算法在所有实验中均优于以往的亚像素映射算法。基于灵敏度分析，ADESM自适应控制参数设置在亚像元制图精度上优于或至少与标准DE算法相当，为遥感影像亚像元制图提供了一种有效的新方法。
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.
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影响因子：
13.5
作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/igarss.2004.1370340
发表时间：
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期刊：
IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium
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