基于量子粒子群算法选择特征的遥感图像分类

针对目前遥感图像分类算法存在精度低、速度慢等问题,提出一种基于量子粒子群算法的遥感图像分类算法,以提高遥感图像的分类效果.首先分析目前遥感图像分类算法存在的不足及其原因;然后提取多种类型的遥感图像原始特征,采用量子粒子群算法对特征进行筛选,以提取对遥感图像分类结果较重要的特征;最后采用最小二乘支持向量机(LSSVM)建立遥感图像分类器,实现遥感图像分类和识别,并进行遥感图像分类的仿真对比实验.实验结果表明,该算法克服了当前遥感图像分类算法存在的局限性,大幅度提高了遥感图像的分类精度,有效减少了图像分类误差,提高了图像分类效率.

Remote Sensing Image Classification Based on SelectingFeaturesof Quantum Particle Swarm Optimization Algorithm

In view of the shortcomings of the low accuracy and slow speed of current remote sensing image classification algorithm, we proposed a remote sensing image classificationalgorithm based on quantum particle swarm optimization to improve the classification effect of remote sensing images. Firstly, we analyzed the shortcomings of the current remote sensing image classification algorithm and its reasons. Secondly, we extracted the original features of various types of remote sensing images, and used the quantum particle swarm algorithm to select features and extract important features of remote sensing image classification results. Finally, using least squares support vector machine, we established classifier for remote sensingimage, realized remote sensing image classification and recognition, and carried out simulation and contrast experiment of remote sensing image classification. The experimental results show that the proposed algorithm overcomes the limitations of current remote sensing image classification algorithms, and the classification accuracy of remote sensing image has been greatly improved, which effectively reduces the error of image classification and improves the efficiency of image classification.