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首页> 外文会议>Conference on Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption with Applications >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：两个进化算法优化群集和多光谱图像中的自动特征选择

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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的模式识别的速度和准确性，例如，在自动目标跟踪中。我们开发了用于利用多光谱图像的两类进化算法。第一种方法处理聚类过程。它确定围绕指定的参考像素周围的像素簇，使得整个群集越来越多地代表搜索对象。初始群体（集群）演变为新集群的群体，每个集群都具有指定的健身分数。该人群经历迭代突变和选择。突变算子改变像素簇集基数和组成。可以应用几个停止标准来终止进化。该进化簇制剂的优点是所得到的簇可以具有任意形状，使得最近其几乎适合搜索模式。第二算法类自动化每个群体成员的特征（中心波长和带宽）的选择。对于图像中的每个像素和每个群体成员，计算到参考组的Mahalanobis距离，并使该像素属于目标。正确和虚假决策的总和定义了一个接收器操作曲线，用于测量群体成员的适应性。基于这种健身，该算法决定将其作为父母用于下一次迭代的人员。

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