提出新型半监督变化检测网络，提升复杂场景下精度与效率
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection
- 基于SAM2构建分层多尺度特征提取框架，融合跨层与层内多尺度信息
- 在四个基准上实现最佳性能，参数量和计算成本均低于现有方法
- 适合遥感图像变化检测任务，尤其适用于标注数据稀缺场景
半监督变化检测（SSCD）旨在利用少量标注数据和大量未标注数据，从双时相遥感图像中识别变化区域。现有方法在复杂场景下表现不佳，面对噪声数据时鲁棒性差，且通常忽略层内多尺度特征，过度依赖跨层融合，损害了不同尺度变化对象的完整性。本文提出HSACNet：一种分层多尺度感知一致性正则化半监督变化检测网络。具体地，采用Segment Anything Model 2（SAM2）的Hiera骨干作为编码器，提取跨层多尺度特征，并通过适配器实现参数高效微调。设计了尺度感知差异注意力模块（SADAM），可精准捕捉层内多尺度变化特征并抑制噪声。此外，引入双重增强一致性正则化策略，有效利用未标注数据。在四个变化检测基准上的大量实验表明，所提方法达到当前最优性能，同时参数量和计算开销显著降低。
原文摘要 · Abstract (English)
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.
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