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Remote sensing data have become increasingly vital in target detection, disaster monitoring, and military surveillance. Abundant pan-sharpening and super-resolution (SR) methods based on deep learning have been proposed and have achieved remarkable performance. However, pan-sharpening requires paired panchromatic (PAN) and multispectral (MS) images, and SR cannot increase the spectral resolution of PAN. Thus, we introduce a computational imaging-based method to recover or produce the incomplete data of single PAN or MS. This work also explores the integration of multiple tasks by a single neural network. We start with SR and colorization, study the feasibility of simultaneously finishing SR colorization, and use a model trained in SR colorization to finish pan-sharpening without MS. A generic neural network, RSI-Net, is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pansharpening. To verify its performance, RSI-Net is compared with state-of-the-art SR and colorization methods. Experiments show that RSI-Net can be competitive in visual effects and evaluation indexes, and it performs well at simultaneous SR colorization, and RSI-Net finishes pan-sharpening only need to input PAN. Our experiments confirm the effect of integrating multiple tasks.

To solve the problems of mistaken coloring and color bleeding in the current colorization methods, an end-to-end deep neural network is proposed to achieve remote sensing image colorization. First, the multi-scale residual receptive filed net is introduced to extract the key features of source image. Second, a color information recovery network is con-structed by using U-Net, complex residual structure, attention mechanism, sequeeze-and-excitation and pixel-shuffle blocks to obtain color result. NWPU-RESISC45 dataset is chosen for model training and validation. Compared with other color methods, the PSNR value of the proposed method is increased by 6-10 dB on average and the SSIM value is increased by 0.05-0.11. In addition, the proposed method also achieves satisfactory color results on RSSCN7 and AID datasets.