A Deep Multitask Convolutional Neural Network for Remote Sensing Image Super-Resolution and Colorization
Jianan Feng, Qian Jiang, Ching-Hsun Tseng, Xin Jin, Ling Liu, Wei Zhou, Shaowen Yao
Abstract
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, remote sensing image improvement network (RSI-Net), is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pan-sharpening. To verify its performance, RSI-Net is compared with the 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 and only needs to input PAN. Our experiments confirm the effect of integrating multiple tasks.