基于蚁群优化与独立特征集的遥感图像实时分类算法

为了提高遥感图像的实时分类准确率与效率，提出了一种基于蚁群优化算法与独立特征集的遥感图像集实时分类算法。首先，提取遥感图像的小波域特征与颜色特征，并且组成特征向量；然后，采用蚁群优化算法对特征空间进行优化，独立地选出每个分类的显著特征集，从而降低每个子特征空间的维度；最终，每个分类独立地训练一个极限学习机分类器，从而实现对遥感图像集的分类。基于公开的遥感图像数据集进行了仿真实验，结果显示本算法实现了较高的分类准确率，并且实现了较高的计算效率。

Real-time classification algorithm of remote sensing images based on ant colony optimization algorithm and independent feature sets

In order to improve the accuracy and efficiency of real-time classification of remote sensing images, this paper proposed a real-time classification algorithm of remote sensing images based on the ant colony optimization algorithm and independent feature sets. Firstly, it abstracted wavelet features and color features of remote sensing images, and the features formed the feature vectors. Then, it adopted the ant colony optimization to optimize the feature space, and it selected the significant feature set of each class independently to reduce the dimension of each feature sub-space. Lastly, it trained an independent extreme learning machine for each class to realize the remote sensing images classification. Simulation experimental results based on the public remote sensing image dataset show that the proposed algorithm realizes a good classification accuracy and computational efficiency.