Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks. However, most existing advances of deep learning methods in the RSI field heavily rely on the features extracted by the manually designed backbone network, which severely hinders the potential of deep learning models due the complexity of RSI and the limitation of prior knowledge. In this paper, we research a new design paradigm for the backbone architecture in RSI recognition tasks, including scene classification, land-cover classification and object detection. A novel one-shot architecture search framework based on weight-sharing strategy and evolutionary algorithm is proposed, called RSBNet, which consists of three stages: Firstly, a supernet constructed in a layer-wise search space is pretrained on a self-assembled large-scale RSI dataset based on an ensemble single-path training strategy. Next, the pre-trained supernet is equipped with different recognition heads through the switchable recognition module and respectively fine-tuned on the target dataset to obtain task-specific supernet. Finally, we search the optimal backbone architecture for different recognition tasks based on the evolutionary algorithm without any network training. Extensive experiments have been conducted on five benchmark datasets for different recognition tasks, the results show the effectiveness of the proposed search paradigm and demonstrate that the searched backbone is able to flexibly adapt different RSI recognition tasks and achieve impressive performance.
翻译:最近,大量深层次的学习方法成功地应用于各种遥感图像识别任务,然而,在高分战略和进化算法的基础上,已成功地应用了大量基于深层次学习方法的现有进展,主要依赖人工设计的骨干网络所提取的特征,这些特征严重妨碍深层次学习模式的潜力,因为RSI的复杂性和先前知识的局限性。在本文件中,我们研究了在高分识别任务中,包括现场分类、土地覆盖分类和对象探测,为RESI识别任务中的主干结构设计了新的范式设计范式。提出了一个新的基于权重共享战略和进化算法的一次性结构搜索框架,称为RSBNet,它由三个阶段组成:首先,在层级搜索空间所建的超级网络在基于全方位单方位单方位培训战略的自行组合大型RSI数据集上进行了预先训练,这严重妨碍了深层次的学习模式。 预先培训的超级网络通过可转换的识别模块配备了不同的识别负责人,并分别对目标数据集集进行了微调,以获得具体任务特制的超级网络。最后,我们寻找基于进化算法的不同识别任务的最佳主干结构结构结构结构结构,而没有任何网络的升级的超级搜索任务。