HPLTS-GAN: A High-Precision Remote Sensing Spatiotemporal Fusion Method Based on Low Temporal Sensitivity

Remote sensing spatiotemporal image fusion is a promising approach to acquire remote sensing data with high spatial and temporal resolution. While most deep neural network-based models have demonstrated high accuracy, they heavily depend on temporal information from the dataset, necessitating a pair of coarse and fine resolution image sets (
$C_{1}-F_{1}$
) taken near the prediction time. This reliance on temporal data makes these models challenging in practical applications. To tackle this challenge, this study introduces a time-insensitive spatiotemporal fusion model. An adaptive spatial distribution transformation (ASDT) module is proposed to enhance the spatiotemporal consistency of fine-resolution images by incorporating the spatial structure of coarse-resolution images. This module aims to improve the model’s performance in tasks less sensitive to time. Furthermore, an encoding-decoding structure is devised to address significant resolution variations in remote sensing images. The encoder uses a multilevel feature extraction (MLFE) module to capture features at multiple levels, reducing information loss and enhancing feature utilization. The decoder includes a cross-scale attention feature fusion and reconstruction (CSAFFIR) module to integrate features of different scales and semantic levels, thereby improving the overall performance of the fused image. Experimental findings from datasets collected by two satellite types demonstrate that the proposed HPLTS-GAN model surpasses existing two-input models in subjective and objective evaluations. In addition, our approach shows competitiveness with current three-input models.