A Cost-effective Solution for Remote Sensing Image Segmentation via Train/Test-Time Adaptation

Abstract

Remote Sensing Image (RSI) segmentation has made significant strides, emerging as a leading solution for interpreting remote sensing data. However, due to the substantial domain gap between different remote sensors and limited computational resources, existing RSI segmentation methods often suffer from poor generalization performance. To address these challenges, we propose a COst-effective and Whole-process Domain Adaptation solution, namely COWDA, which adapts models at both the train and test time through three key phases: 1) Source-data Domain Alignment: We employ traditional image stylization techniques to translate source images into target-style alternatives, which avoids the computationally intensive need for auxiliary neural network models. 2) Target-data Train-time Fine-tuning: We propose a joint positive and negative learning (JPNL) algorithm that adds both positive and negative samples to effectively learn domain-invariant knowledge from noisy pseudo-labeled target data. 3) Test-time Adaptation: We propose an entropy-weighted test-time adaptation strategy to update the trained model with online test samples, further enhancing its performance. Extensive experiments on two widely-used domain adaptation benchmarks for remote sensing show that COWDA improves state-of-the-art counterparts by 1.4% and 2.6% F1 scores, respectively.