基于双通道深度密集特征融合的遥感影像分类

为了提高遥感图像场景分类中特征有效利用率，进而提高遥感影像分类精度，采用基于双通道深度密集特征融合的遥感影像分类方法，进行了理论分析和实验验证。首先通过构建复合密集网络模型, 分别提取图像卷积层特征和全连接层特征；然后为挖掘、利用图像深层信息，通过视觉词袋模型将提取的深层卷积层特征进行重组编码，捕获图像深层局部特征；最后采用线性加权方式将局部和全局特征融合、分类。结果表明，选用数据集UC Merced Land-Use和NWPU-RESISC45进行实验，取得的分类精度分别为93.81%和92.62%。该方法充分利用局部特征和全局特征的互补性，能实现图像深层信息的充分利用和表达。

Remote sensing image classification based on dual-channel deep dense feature fusion

In order to improve the effective utilization of features in remote sensing image scene classification and achieve the purpose of improving the accuracy of remote sensing image classification, a remote sensing image classification method based on dual-channel depth-dense feature fusion was used for theoretical analysis and experimental verification. First, the image convolution layer features and fully connected layer features was separated extracted by constructing a composite dense convolutional network model. In order to exploit the deep information of the image, the deep convolutional layer features extracted by the model were recombined and encoded by the bag of visual words to capture the deep local features of the image. Finally the linear and weighted methods were used to fuse local and global features and then classify them. The results show that using the datasets UC Merced Land-Use and NWPU-RESISC45 for experiments, the classification accuracy obtained is 93.81% and 92.62%, respectively. This method makes full use of the complementarity of local features and global features to achieve the full expression of deep image information.