Remote Sensing Image Colorization Based on Joint Stream Deep Convolutional Generative Adversarial Networks

With the development of deep neural networks, especially generation networks, gray image coloring technology has made great progress. As one of the fields, remote sensing image colorization needs to be solved urgently. This is because remote sensing images cannot obtain clear color images due to the limitations of shooting equipment and transmission equipment. Compared with ordinary images, remote sensing images are characterized by the uneven spatial distribution of objects, therefore, it is a great challenge to ensure the spatial consistency of coloring. To embrace this challenge, we propose a new joint stream DCGAN including a micro stream and a macro stream, in which the latter is set as a prior to constrain the former for colorization. In addition, the Low-level Correlation Feature Extraction (LCFE) module is proposed to obtain the salient shallow detail feature with global correlation, which is used to enhance the global constraints as well as supplement the low-level information to the micro stream. What's more, we propose the Gated Selection (GSM) module by selecting useful information using a gated scheme to fuse features from two streams appropriately. Comprehensive comparison and ablation experiments are implemented and verify the proposed method performs surpasses other methods in both qualitative and quantitative metrics.