Foundation models have reshaped the landscape of Remote Sensing (RS) by
enhancing various image interpretation tasks. Pretraining is an active research
topic, encompassing supervised and self-supervised learning methods to
initialize model weights effectively. However, transferring the pretrained
models to downstream tasks may encounter task discrepancy due to their
formulation of pretraining as image classification or object discrimination
tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for
RS foundation models to address this issue. Using a shared encoder and
task-specific decoder architecture, we conduct multi-task supervised
pretraining on the SAMRS dataset, encompassing semantic segmentation, instance
segmentation, and rotated object detection. MTP supports both convolutional
neural networks and vision transformer foundation models with over 300 million
parameters. The pretrained models are finetuned on various RS downstream tasks,
such as scene classification, horizontal and rotated object detection, semantic
segmentation, and change detection. Extensive experiments across 14 datasets
demonstrate the superiority of our models over existing ones of similar size
and their competitive performance compared to larger state-of-the-art models,
thus validating the effectiveness of MTP.

本研究基于共享编码器和任务特定解码器架构，对遥感基础模型进行多任务监督预训练，包括语义分割、实例分割和旋转目标检测。随后在不同的遥感下游任务上对预训练模型进行微调，并通过 14 个数据集的广泛实验验证了我们模型相对于同样规模的现有模型的优越性以及与更大型的最先进模型的竞争性表现，从而验证了多任务预训练的有效性。