Learning discriminative and robust features is crucial in remote sensing image processing. Many of the currently used approaches are based on Convolutional Neural Networks (CNNs). However, such approaches may not effectively capture various different semantic objects of remote sensing images. To overcome this limitation, we propose a novel end-to-end deep multi-feature fusion network (DMFN). DMFN combines two different deep architecture branches for feature representations; the global and local branch. The global branch, which consists of three losses, is used to learn discriminative features from the whole image. The local branch is then used in the partitioning of the entire image into multiple strips in order to obtain local features. The two branches are then combined, used to learn fusion feature representations for the image. The proposed method is an end-to-end framework during training. Comprehensive validation experiments on two public datasets indicate that relative to existing deep learning approaches, this strategy is superior for both retrieval and classification tasks.

学习判别和强大的功能对于遥感图像处理至关重要。当前使用的许多方法都基于卷积神经网络（CNN）。然而，这样的方法可能不能有效地捕获遥感图像的各种不同的语义对象。为了克服此限制，我们提出了一种新颖的端到端深度多特征融合网络（DMFN）。DMFN结合了两个不同的深度架构分支来进行特征表示；全球和本地分支机构。包含三个损失的全局分支用于从整个图像中学习区分特征。然后，将局部分支用于将整个图像划分为多个条带，以获得局部特征。然后将这两个分支合并，用于学习图像的融合特征表示。所提出的方法是训练过程中的端到端框架。在两个公共数据集上进行的全面验证实验表明，相对于现有的深度学习方法，该策略在检索和分类任务方面均更为出色。