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Evolutionary computation for information extraction from remotely sensed imagery.

机译：用于从遥感影像中提取信息的进化计算。

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Automated and semi-automated techniques have been researched as an alternative way to reduce human interaction and thus improve the information extraction process from imagery. This research developed an innovative methodology by integrating machine learning algorithms with image processing and remote sensing procedures to form the evolutionary framework. In this biologically-inspired methodology, non-linear solutions are developed by iteratively updating a set of candidate solutions through operations such as: reproduction, competition, and selection. Uncertainty analysis is conducted to quantitatively assess the system's variability due to the random generation of the initial set of candidate solutions, from which the algorithm begins. A new convergence approach is proposed and results indicate that it not only reduces the overall variability of the system but also the number of iterations needed to obtain the optimal solution. Additionally, the evolutionary framework is evaluated in solving different remote sensing problems, such as: non-linear inverse modeling, integration of image texture with spectral information, and multitemporal feature extraction. The investigations in this research revealed that the use of evolutionary computation to solve remote sensing problems is feasible. Results also indicate that, the evolutionary framework reduces the overall dimensionality of the data by removing redundant information while generating robust solutions regardless of the variations in the statistics and the distribution of the data. Thus, signifying that the proposed framework is capable of mathematically incorporating the non-linear relationship between features into the final solution.
机译：已经研究了自动化和半自动化技术，作为减少人与人之间的互动并因此改善了从图像中提取信息的替代方法。这项研究通过将机器学习算法与图像处理和遥感程序相集成以形成进化框架，从而开发了一种创新的方法。在这种受生物启发的方法中，非线性解决方案是通过以下操作来迭代更新一组候选解决方案而开发的：复制，竞争和选择。进行不确定性分析以定量评估系统的可变性，这归因于候选算法初始集合的随机生成，算法从该候选集合开始。提出了一种新的收敛方法，结果表明，它不仅减少了系统的总体可变性，而且还减少了获得最佳解所需的迭代次数。另外，在解决不同的遥感问题时评估了演化框架，例如：非线性逆建模，图像纹理与光谱信息的集成以及多时相特征提取。这项研究的调查表明，使用进化计算来解决遥感问题是可行的。结果还表明，进化框架通过删除冗余信息，同时生成健壮的解决方案，从而降低了数据的整体维度，而与统计数据和数据分布的变化无关。因此，表明所提出的框架能够在数学上将特征之间的非线性关系纳入最终解决方案。

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