Due to factors such as thick cloud cover and sensor limitations, remote
sensing images often suffer from significant missing data, resulting in
incomplete time-series information. Existing methods for imputing missing
values in remote sensing images do not fully exploit spatio-temporal auxiliary
information, leading to limited accuracy in restoration. Therefore, this paper
proposes a novel deep learning-based approach called MS2TAN (Multi-scale Masked
Spatial-Temporal Attention Network), for reconstructing time-series remote
sensing images. Firstly, we introduce an efficient spatio-temporal feature
extractor based on Masked Spatial-Temporal Attention (MSTA), to obtain
high-quality representations of the spatio-temporal neighborhood features in
the missing regions. Secondly, a Multi-scale Restoration Network consisting of
the MSTA-based Feature Extractors, is employed to progressively refine the
missing values by exploring spatio-temporal neighborhood features at different
scales. Thirdly, we propose a ``Pixel-Structure-Perception'' Multi-Objective
Joint Optimization method to enhance the visual effects of the reconstruction
results from multiple perspectives and preserve more texture structures.
Furthermore, the proposed method reconstructs missing values in all input
temporal phases in parallel (i.e., Multi-In Multi-Out), achieving higher
processing efficiency. Finally, experimental evaluations on two typical missing
data restoration tasks across multiple research areas demonstrate that the
proposed method outperforms state-of-the-art methods with an improvement of
0.40dB/1.17dB in mean peak signal-to-noise ratio (mPSNR) and 3.77/9.41
thousandths in mean structural similarity (mSSIM), while exhibiting stronger
texture and structural consistency.

提出了一种基于深度学习的新方法 MS2TAN (Multi-scale Masked Spatial-Temporal Attention Network)，用于重建时间序列的遥感图像，通过充分利用空时辅助信息，得到更高的恢复准确性。