Spatiotemporal Fusion Algorithm Based on Multikernal Adaptive Network

Spatiotemporal fusion (STF) technology is an effective means to address the challenge of balancing temporal and spatial resolutions for single satellite sensors. Remote sensing imagery exhibits substantial scale variations in different ground objects. However, existing CNN-based models employ a fixed receptive field during feature extraction, leading to a lack of dynamic adjustment capability when capturing information at different scales, which limits the fusion accuracy. To overcome this limitation, our study proposes the Multi-Kernel Adaptive Network (MKAN), specifically designed for STF tasks. The network incorporates our proposed Dynamic Visual Processing Module (DVPM) to achieve adaptive perception and intelligent fusion of multi-scale geophysical features. Furthermore, to enhance the geometric consistency of DVPM during the process of cross-scale feature fusion of geophysical features, we design the Multi-Dimensional Perceptual Attention Module (MDPA), which integrates channel and spatial domain information enhancement techniques for precise feature identification and optimized pixel-level resource allocation. To comprehensively evaluate the performance advantages of MKAN, we conducted thorough experimental assessments on three datasets and benchmarked it against five spatiotemporal fusion algorithms. The experimental results demonstrate that MKAN excels in multiple evaluation metrics. Compared to optimal comparison algorithms, MKAN increases structural similarity by an average of 1.4% and reduces global dimensionless error by an average of 4.4%, fully demonstrating the rationality and superiority of its design. In addition, this chapter explores the effectiveness of the MKAN network structure and assesses the contributions of the DVPM and MDPA modules to the overall algorithm performance through meticulous ablation experiments conducted from various perspectives.