  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.
