SSLCD-ADAPT: A hierarchical framework for label-efficient cross-domain semantic change detection in complex environments

Semantic Change Detection (SCD) plays a crucial role in understanding land surface dynamics, from urban expansion and deforestation to disaster impact assessment. However, despite the success of deep learning models in SCD tasks, their real-world deployment faces two critical challenges: significant domain shifts between geographically distinct regions and the prohibitive cost of data annotation for new locations. Models trained on public benchmarks, predominantly from developed countries, experience substantial performance degradation when applied to regions with different characteristics, such as Indian cities, due to inter-domain variance in sensors, atmospheric conditions, and landscapes. Additionally, substantial intra-domain variance within target regions compounds this problem, necessitating robust solutions that operate with limited labels.

To address these challenges, we propose SSLCD-Adapt, a novel hierarchical framework for label-efficient, cross-domain SCD that tackles inter-domain, intra-domain, and label constraints through a three-stage process. First, we employ Change-Enhanced Self-Supervised Pre-Training, where change representations are learned directly from unlabeled bi-temporal image pairs from the source domain using the FSC-180K benchmark dataset. Through the application of a Barlow Twins objective to fuse features from distorted views, the model learns invariant characteristics of change without manual annotation, providing superior initialization compared to ImageNet pre-trained models, which differ significantly from remote sensing imagery.

Second, Domain Alignment bridges the data distribution gap between the source (FSC-180K) and the target (six Indian cities) domains. The source encoder remains frozen while the target encoder undergoes training in only three layers within an adversarial setup. We employ a Domain-Adversarial Neural Network (DANN) that incorporates a Gradient Reversal Layer (GRL) with a Maximum Mean Discrepancy (MMD) loss to align feature distributions without requiring target labels. The domain classifier maximizes the H-divergence between the source and target domains, while GRL reverses the gradients, forcing target encoders to generate features similar to those of the source encoders, thereby achieving alignment in feature space and minimizing inter-domain variance.

Third, the trained target encoder undergoes Progressive Domain-specific fine-tuning using limited target labeled data. The encoder trains for one-third of the epochs on target data, then for two-thirds of the epochs using city-specific batches with domain-specific batch normalization for each city, effectively minimizing intra-domain variance between the six Indian cities. Figure 1 demonstrates the complete SSLCD-Adapt architecture.

Figure 1: Proposed SSLCD-Adapt Architecture