Deep remote fusion: development of improved deep CNN with atrous convolution-based remote sensing image fusion

Various remote sensing images seek more attention with their high temporal and spatial resolution in the applications of earth science. Conversely, it is a difficult task for a single satellite to enhance high-quality images owing to its cost and technical constraints. It aims to suggest a novel remote sensing image fusion model for overcoming the limitations of the existing fusion approaches, where effective fusion rules were generated. Initially, the low frequency of MS image is used to enhance the PAN image using the optimized wavelet transform. Then, the enhanced PAN and MS images are used by the Improved Deep Convolutional Neural Network with Atrous Convolutions (IDCNN-AC) for getting the high-quality fused images. Further, the parameters in the IDCNN-AC are optimized using Hybrid Harris Hawks Dingo Optimization (HHHDO) for enhancing the fusion performance. Finally, the simulation outcome shows the efficient performance of the proposed image fusion model using different quantitative measures.