Adaptive Transformer for Multitemporal Thick Cloud Reconstruction With Low-Intensity Reference

Optical satellite imagery has become indispensable for Earth observation, yet thick cloud contamination severely impedes continuous monitoring capabilities. Current temporal-based methodologies present dual limitations: conventional approaches suffer from inefficient processing speeds and poor robustness, while existing deep learning-based solutions exhibit constrained temporal adaptability by relying on limited temporal information from single or fixed-period images. These inherent shortcomings consequently hinder their scalability in large-scale real-world applications. To address this challenge, we propose a multitemporal adaptive cloud removal Transformer (MACRT), which uses a temporal aggregation strategy to integrate information from multiple reference images, reduce the constraint of reference image selection, and provide a more practical cloud removal solution. Since neighboring pixels of remote sensing images have strong correlations, the proposed network architecture introduces a novel spatial–spectral multihead attention (SSMHA) module. This module decomposes the attention computation into two branches: coarse-resolution spatial and spectral, which work together to capture long-range dependencies across both dimensions while maintaining a compact parameter count. Extensive experiments on multispectral datasets (Landsat and Sentinel-2) demonstrate the superior performance of MACRT. Quantitative evaluations on Landsat imagery reveal improvements of 1.7% in SSIM and 2.1° in SAM over state-of-the-art (SOTA) methods, achieving enhanced structural preservation and spectral accuracy. The source code, pretrained models, and all experimental results will be publicly available at https://github.com/Zhangyu-student/ACRT upon the paper’s acceptance.