Class-Aware Domain Adaptation for Coastal Land Cover Mapping Using Optical Remote Sensing Imagery

Abstract

Coastal land cover mapping is a significant yet challenging pixel-level segmentation task. Domain shift between optical remote sensing imagery will give rise to remarkable performance degradation for deep supervised methods. Besides, the ground objects characterized with interclass variance and class imbalance may further aggravate the adverse effect. Traditional adversary-based domain adaptation algorithms always leverage a binary discriminator to conduct global adaptation, ignoring the detailed class information. In this article, we develop a novel class-aware domain adaptation method to address these issues. Unlike the naive single one, we propose a joint local and global adversarial adaptation framework to separately execute class-specific and global domain alignment on feature and output spaces. For the former, the introduced classwise discriminator possesses different strategies to extract labels for both data domains. Meanwhile, we restore to entropy minimization to produce high-confident target prediction rather than using the early generated pseudo label with high confidence. Furthermore, we additionally adopt comprehensive reweighting on the supervised segmentation loss to track the class imbalance problem. This manner mainly comprises the sample-based median frequency balancing and the focal loss function for the minority and hard classes. We measure the proposed method on two typical coastal datasets and compare it with other state-of-the-art models. The experimental results confirm its excellent and competitive performance on cross-domain land cover mapping.

Keywords

• Adversarial learning
• class balancing
• coastal land cover mapping (CLCM)
• domain adaptation
• entropy minimization (EM)