Temporally-Similar Structure-Aware Spatiotemporal Fusion of Satellite Images

2025-08-15

AI Summary

In satellite image spatiotemporal fusion, noise-induced spatial detail loss, excessive smoothing, and artifact generation remain critical challenges. To address these, this paper proposes TSSTF—a novel spatiotemporal fusion framework. Methodologically, TSSTF introduces a temporal-guided total variation regularization coupled with explicit edge constraints to jointly preserve fine-scale spatial structures while ensuring temporal consistency across the sequence. It further employs a preconditioned primal-dual splitting algorithm, leveraging a reference high-resolution image to enable efficient and robust spatiotemporal regularization. Experimental results demonstrate that TSSTF consistently outperforms state-of-the-art methods under both noise-free and noisy conditions; notably, it achieves substantial improvements in structural fidelity under strong noise, with enhanced robustness and favorable parameter generalizability across diverse scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

This paper proposes a novel spatiotemporal (ST) fusion framework for satellite images, named Temporally-Similar Structure-Aware ST fusion (TSSTF). ST fusion is a promising approach to address the trade-off between the spatial and temporal resolution of satellite images. In real-world scenarios, observed satellite images are severely degraded by noise due to measurement equipment and environmental conditions. Consequently, some recent studies have focused on enhancing the robustness of ST fusion methods against noise. However, existing noise-robust ST fusion approaches often fail to capture fine spatial structure, leading to oversmoothing and artifacts. To address this issue, TSSTF introduces two key mechanisms: Temporally-Guided Total Variation (TGTV) and Temporally-Guided Edge Constraint (TGEC). TGTV is a novel regularization function that promotes spatial piecewise smoothness while preserving structural details, guided by a reference high spatial resolution image acquired on a nearby date. TGEC enforces consistency in edge locations between two temporally adjacent images, while allowing for spectral variations. We formulate the ST fusion task as a constrained optimization problem incorporating TGTV and TGEC, and develop an efficient algorithm based on a preconditioned primal-dual splitting method. Experimental results demonstrate that TSSTF performs comparably to state-of-the-art methods under noise-free conditions and outperforms them under noisy conditions. Additionally, we provide a comprehensive set of recommended parameter values that consistently yield high performance across diverse target regions and noise conditions, aiming to enhance reproducibility and practical utility.

Problem

• Enhances satellite image fusion robustness against noise
• Preserves fine spatial structure to avoid oversmoothing
• Improves consistency in edge locations between images

Innovation

• Temporally-Guided Total Variation for spatial smoothness
• Temporally-Guided Edge Constraint for edge consistency
• Preconditioned primal-dual splitting optimization algorithm