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首页> 外文会议>Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications; Proceedings of SPIE-The International Society for Optical Engineering; vol.6700 >Two evolutionary algorithms optimize clusters and automate feature selection in multispectral images
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Two evolutionary algorithms optimize clusters and automate feature selection in multispectral images

机译：两种进化算法可在多光谱图像中优化聚类并自动进行特征选择

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摘要

Evolutionary computation can increase the speed and accuracy of pattern recognition in multispectral images, for example, in automatic target tracking. We have developed two classes of evolutionary algorithms for exploiting multispectral imagery. The first method treats the clustering process. It determines a cluster of pixels around specified reference pixels so that the entire cluster is increasingly representative of the search object. An initial population (of clusters) evolves into populations of new clusters, with each cluster having an assigned fitness score. This population undergoes iterative mutation and selection. Mutation operators alter both the pixel cluster set cardinality and composition. Several stopping criteria can be applied to terminate the evolution. An advantage of this evolutionary cluster formulation is that the resulting cluster may have an arbitrary shape so that it most nearly fits the search pattern. The second algorithm class automates the selection of features (the center-wavelength and the bandwidth) for each population member. For each pixel in the image and for each population member, the Mahalanobis distance to the reference set is calculated and a decision is made whether or not this pixel belongs to a target. The sum of correct and false decisions defines a Receiver Operating Curve, which is used to measure the fitness of a population member. Based on this fitness, the algorithm decides which population members to use as parents for the next iteration.
机译：进化计算可以提高多光谱图像中模式识别的速度和准确性，例如在自动目标跟踪中。我们已经开发了两类用于开发多光谱图像的进化算法。第一种方法处理聚类过程。它确定指定参考像素周围的像素簇，以便整个簇越来越代表搜索对象。 （群集的）初始种群演变为新群集的种群，每个群集都有一个分配的适合度得分。该种群经历迭代突变和选择。变异算子会同时更改像素簇集的基数和组成。可以应用几个停止标准来终止演化。这种进化聚类公式的一个优点是，所得聚类可以具有任意形状，从而使其最适合搜索模式。第二个算法类自动为每个总体成员选择特征（中心波长和带宽）。对于图像中的每个像素以及每个人口成员，计算到参考集的马氏距离，并确定该像素是否属于目标。正确和错误决策的总和定义了接收方操作曲线，该曲线用于测量总体成员的适应性。基于此适合度，该算法决定将哪些人口成员用作下一次迭代的父对象。

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