MSP-O: A Two-Stage Mask Pretraining Method With Object Focus for Remote Sensing Object Detection

Pretraining is showing fundamental importance in computer vision, to initialize the backbones of deep learning models with large natural image datasets, e.g., ImageNet. However, there is seldom a special pretraining technique focused on remote sensing object detection due to the distinct characteristics of remote sensing images, such as specific imaging angles, complex backgrounds, and various target categories. This article aims to bridge the gap between natural image data and remote sensing image data and to facilitate pretraining for the specific object detection task. This article introduces masked remote sensing pretraining with object (MSP-O), an innovative two-stage masked pretraining method. Traditional single-stage pretraining frameworks aim to extract intrinsic features from large-scale image datasets to learn object discrimination and localization ability. MSP-O builds upon this by adding a second-stage pretraining phase to learn specific features related to remote sensing objects of interest themselves, facilitating the downstream object detection task. Specifically, MSP-O incorporates a pixel-level restoration task focused on the objects of interest, guiding the model to prioritize the objects to be detected. Compared with traditional pretraining methods, MSP-O is able to learn feature representations that are more relevant to the detection task, especially when the data scale is limited. Extensive experiments on the DOTA and DIOR datasets demonstrate the effectiveness of MSP-O, showing substantial improvements in detection performance compared with conventional methods.