基于深度学习特征融合的遥感图像场景分类应用

针对传统手工特征方法无法有效提取整体图像深层信息的问题，本文提出一种基于深度学习特征融合的场景分类新方法.利用灰度共生矩阵（GLCM）和局部二值模式（LBP）提取具有相关空间特性的纹理特征和局部纹理特征的浅层信息；通过基于AlexNet迁移学习网络提取图像的深层信息，在去除最后一层全连接层的同时加入一层256维的全连接层作为特征输出；将两种特征进行自适应融合，最终输入到网格搜索算法优化的支持向量机（GS-SVM）中对遥感图像进行场景分类识别.在公开数据集UC Merced的21类目标数据和RSSCN7的7类目标数据的实验结果表明，5次实验的平均准确率分别达94.77%和93.79%.该方法可有效提升遥感图像场景的分类精度.

Remote sensing image scene classification based on deep learning feature fusion

In view that traditional manual feature extraction method cannot effectively extract the overall deep image information, a new method of scene classification based on deep learning feature fusion is proposed for remote sensing images.First, the Grey Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) are used to extract the shallow information of texture features with relevant spatial characteristics and local texture features as well;second, the deep information of images is extracted by the AlexNet migration learning network, and a 256-dimensional fully connected layer is added as feature output while the last fully connected layer is removed;and the two features are adaptively integrated, then the remote sensing images are classified and identified by the Grid Search optimized Support Vector Machine (GS-SVM).The experimental results on 21 types of target data of the public dataset UC Merced and 7 types of target data of RSSCN7 produced average accuracy rates of 94.77% and 93.79%, respectively, showing that the proposed method can effectively improve the classification accuracy of remote sensing image scenes.