This paper presents a novel framework that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image classification. The proposed framework consists of four main modules. The first module aims to extract preliminary local descriptors by considering that RS image bands can be associated with different spatial resolutions. To this end, we introduce a K-Branch CNN in which each branch aims at extracting descriptors of image bands that have the same spatial resolution. The second module aims to model spatial relationship among local descriptors. To this end, we propose a Bidirectional RNN architecture in which Long Short-Term Memory nodes enrich local descriptors by considering spatial relationships of local areas (image patches). The third module aims to define multiple attention scores for local descriptors. To this end, we introduce a novel patch-based multi-attention mechanism that takes into account the joint occurrence of multiple land-cover classes and provides the attention-based local descriptors. The last module aims to employ these descriptors for multi-label RS image classification. Experimental results obtained on our large-scale Sentinel-2 benchmark archive (called as BigEarthNet) show the effectiveness of the proposed framework compared to a state of the art method.

翻译：本文介绍了一个在多标签遥感图像分类背景下共同利用革命神经网络(CNN)和经常神经网络(RNN)的新框架。拟议框架由四个主要模块组成。第一个模块旨在提取初步的当地描述符,考虑RS图像波段可以与不同的空间分辨率相联系。为此,我们引入了一个K-BranchCNN,其中每个分支都旨在提取具有相同空间分辨率的图像波段的描述符。第二个模块旨在模拟地方描述符之间的空间关系。为此,我们提出一个双向 RNN 结构,其中长期短期记忆节点通过考虑当地空间关系(模拟补丁)来丰富当地描述符。第三个模块旨在确定当地描述符的多重关注分数。为此,我们引入了一个新型的基于补丁多保护机制,其中考虑到多个土地覆盖等级的共发情况,并提供基于关注的本地描述符。最后一个模块旨在将这些描述符用于多标签的 RNNE图像分类(长期短期记忆节点)通过考虑当地空间关系(模拟补丁补丁)来丰富当地描述描述符号。第三个模块旨在确定当地描述器的多标签,用以显示我们大规模地球定位基准的图像基准的大规模测试结果。