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

• URL: http://arxiv.org/abs/2406.13358v2
• Date: Mon, 18 Nov 2024 21:36:53 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, Li Cao,
• Abstract summary: Existing methods for imputing missing values in remote sensing images fail to fully exploit auxiliary information. This paper proposes a deep learning-based novel approach called MS2 for reconstructing time-series remote sensing images.
• Score: 0.6675733925327885
• License:
• Abstract: Remote sensing images often suffer from substantial data loss due to factors such as thick cloud cover and sensor limitations. Existing methods for imputing missing values in remote sensing images fail to fully exploit spatiotemporal auxiliary information, which restricts the accuracy of their reconstructions. To address this issue, 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. First, we introduce an efficient spatiotemporal feature extractor based on Masked Spatial-Temporal Attention (MSTA) to capture high-quality representations of spatiotemporal neighborhood features surrounding missing regions while significantly reducing the computational complexity of the attention mechanism. Second, a Multi-Scale Restoration Network composed of MSTA-based Feature Extractors is designed to progressively refine missing values by exploring spatiotemporal neighborhood features at different scales. Third, we propose a "Pixel-Structure-Perception" Multi-Objective Joint Optimization method to enhance the visual quality of the reconstructed results from multiple perspectives and to preserve more texture structures. Finally, quantitative experimental results under multi-temporal inputs on two public datasets demonstrate that the proposed method outperforms competitive approaches, achieving a 9.76%/9.30% reduction in Mean Absolute Error (MAE) and a 0.56 dB/0.62 dB increase in Peak Signal-to-Noise Ratio (PSNR), along with stronger texture and structural consistency. Ablation experiments further validate the contribution of the core innovations to imputation accuracy.

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