While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial features due to neglecting global context and incomplete change learning. Additionally, transformer networks struggle with low-level details. RCTNet addresses these limitations by introducing \textbf{(1)} an early fusion backbone to exploit both spatial and temporal features early on, \textbf{(2)} a Cross-Stage Aggregation (CSA) module for enhanced temporal representation, \textbf{(3)} a Multi-Scale Feature Fusion (MSF) module for enriched feature extraction in the decoder, and \textbf{(4)} an Efficient Self-deciphering Attention (ESA) module utilizing transformers to capture global information and fine-grained details for accurate change detection. Extensive experiments demonstrate RCTNet's clear superiority over traditional RS image CD methods, showing significant improvement and an optimal balance between accuracy and computational cost.

翻译：尽管深度学习，特别是卷积神经网络（CNN），已彻底革新了遥感（RS）变化检测（CD）领域，但现有方法常因忽略全局上下文和不完整的变化学习而遗漏关键特征。此外，Transformer网络在处理低层次细节方面存在困难。RCTNet通过引入以下机制应对这些局限：\textbf{（1）}一种早期融合主干网络，以在早期阶段同时利用空间和时间特征；\textbf{（2）}一个跨阶段聚合（CSA）模块，用于增强时间表征；\textbf{（3）}一个多尺度特征融合（MSF）模块，在解码器中实现丰富的特征提取；以及\textbf{（4）}一个高效自解析注意力（ESA）模块，利用Transformer捕获全局信息和细粒度细节，以实现精确的变化检测。大量实验证明，RCTNet相较于传统遥感图像变化检测方法具有明显优势，在精度与计算成本之间实现了显著改进和最佳平衡。