MGC: MLP-Guided CNN Pre-Training using A Small-scale Dataset for Remote Sensing Images

To overcome the inherent domain gap between natural images and remote sensing images (RSIs), it is highly desirable to develop pre-training methods specifically for RSIs. Considering the lack of widely recognized large-scale benchmarks like ImageNet in the RSI community and limited computational resources, this paper proposes MGC, a method that employs an MLP to guide the pre-training of a CNN from small-scale datasets for RSIs. MGC has two encoders, each consisting of a CNN branch and an MLP branch. We first contrast pairwise samples from same type of branches or different type of branches across the encoders, and employ a positive-pair guidance strategy to explore consistency. Due to the inherent locality issue of shallow layers in a CNN, the CNN branches often do not attend to correct foreground regions such as objects, regions of interest, and land coverage. Therefore, we further propose an attention guidance strategy to guide the CNN branches to focus on foreground regions and learn discriminative representations effectively. The proposed MGC method is validated by pre-training a CNN model using the MGC and applying it to different downstream tasks including scene classification, rotated object detection, semantic segmentation and change detection on ten datasets. Results have confirmed the effectiveness of the proposed MGC. Our code will be released at: https://github.com/benesakitam/MGC.