Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network

• URL: http://arxiv.org/abs/2406.13358v1
• Date: Wed, 19 Jun 2024 09:05:05 GMT
• Title: Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network
• Authors: Zaiyan Zhang, Jining Yan, Yuanqi Liang, Jiaxin Feng, Haixu He, Wei Han,
• Abstract summary: Methods for imputing missing values in remote sensing images do not fully exploit auxiliary information. Deep learning-based approach called MS2 proposes time-series reconstructing remote sensing images. Method outperforms state-of-the-art methods with an improvement of 0.40dB/1.17dB in mean peak signal-to-signal ratio.
• Score: 2.230457109765141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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