Work

A Bidirectional Cross Spatiotemporal Fusion Network with Spectral Restoration for Remote Sensing Imagery

Year: 2025

Type: article

Abstract: Existing deep learning-based spatiotemporal fusion (STF) methods for remote sensing imagery often focus exclusively on capturing temporal changes or enhancing spatial details while failing to fully leverage spectral information from coarse images. To address these limitations, we propose a Bidirectional Cross Spatiotemporal Fusion Network with Spectral Restoration (BCSR-STF). The network integrates temporal and spatial information using a Bidirectional Cross Fusion (BCF) module and restores spectral fidelity through a Global Spectral Restoration and Feature Enhancement (GSRFE) module, which combines Adaptive Instance Normalization and spatial attention mechanisms. Additionally, a Progressive Spatiotemporal Feature Fusion and Restoration (PSTFR) module employs multi-scale iterative optimization to enhance the interaction between high- and low-level features. Experiments on three datasets demonstrate the superiority of BCSR-STF, achieving significant improvements in capturing seasonal variations and handling abrupt land cover changes compared to state-of-the-art methods.

Source: Applied Sciences

DOI: 10.3390/app15126649

Authors: Dandan Zhou, Ke Wu, Gang Xu

Institutions: China University of Geosciences, Zhejiang College of Security Technology

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896