  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.
