中文摘要：

为了提高遥感图像场景分类精度,提出了一种基于增广LDA（Latent Dirichlet Allocation）模型的特征选择算法.首先对图像进行尺度不变特征变换、颜色直方图、几何模糊特征、局域二值模式和Gabor纹理特征提取,然后引入一种改进的自动选择特征算法,通过交叉验证选出最具针对性的特征组合,再利用LDA将高维特征组合进行降维,最后使用正则化逻辑回归分类器完成场景分类.实验结果表明,与其他特征组合相比,经自动选择后的特征组合可以有效提高遥感图像场景分类的精度.

英文摘要：

To improve the accuracy in scene categorization of satellite images,this paper presents an algorithm of feature selection based on augmented LDA（Latent Dirichlet Allocation） model,and the algorithm is improved,which can automatically selects features from the features-pool.This method firstly extracts five kinds of features（SIFT,Geometric Blur,LBP,Gabor and Color histogram） from each image,and during the cross-validation,the combined features,which have the best performance over the dataset are got.Next,the dimensionality of the combined features is reduced by using LDA.Finally the regularized logistic regression classifier are employed to achieve the classification.Compared with other feature combination,the experimental results demonstrate that,the combination of the automatically selected features can improve the accuracy of scene categorization of satellite images effectively.