HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection
This addresses the challenge of detecting changes in complex remote sensing scenarios with limited labeled data, representing an incremental improvement over existing methods.
The paper tackles the problem of semi-supervised change detection in remote sensing images by proposing HSACNet, which integrates SAM2 with a scale-aware module and consistency regularization, achieving state-of-the-art performance across four benchmarks with reduced parameters and computational cost.
Semi-supervised change detection (SSCD) aims to detect changes between bi-temporal remote sensing images by utilizing limited labeled data and abundant unlabeled data. Existing methods struggle in complex scenarios, exhibiting poor performance when confronted with noisy data. They typically neglect intra-layer multi-scale features while emphasizing inter-layer fusion, harming the integrity of change objects with different scales. In this paper, we propose HSACNet, a Hierarchical Scale-Aware Consistency regularized Network for SSCD. Specifically, we integrate Segment Anything Model 2 (SAM2), using its Hiera backbone as the encoder to extract inter-layer multi-scale features and applying adapters for parameter-efficient fine-tuning. Moreover, we design a Scale-Aware Differential Attention Module (SADAM) that can precisely capture intra-layer multi-scale change features and suppress noise. Additionally, a dual-augmentation consistency regularization strategy is adopted to effectively utilize the unlabeled data. Extensive experiments across four CD benchmarks demonstrate that our HSACNet achieves state-of-the-art performance, with reduced parameters and computational cost.