提出了一种融合全局和局部深度特征(GLDFB)的视觉词袋模型。通过视觉词袋模型将深度卷积神经网络提取的多个层次的高层特征进行重组编码并融合,利用支持向量机对融合特征进行分类。充分利用包含场景局部细节信息的卷积层特征和包含场景全局信息的全连接层特征,完成对遥感影像场景的高效表达。通过对两个不同规模的遥感图像场景数据集的实验研究表明,相比现有方法,所提方法在高层特征表达能力和分类精度方面具有显著优势。

A global and local deep feature based (GLDFB) bag-of-visual-words (BoVW) model is proposed. The high-level features extracted from the deep convolutional neural network are reorganized and encoded by the BoVW model and the fusion features are classified by the support vector machine. The features from the convolutional layer containing the local details and the fully-connected layer containing the global information of scenes are fully used and thus the efficient expressions of the remote sensing image scenes are formed. The experimental results on two remote sensing image scene datasets with different scales show that, compared with the existing methods, the proposed method possesses unique advantages in the representation ability and the classification accuracy of high-level features.