DARK: Few-Shot Remote-Sensing Colorization Using Label-Conditioned Color Injection

Abstract

Satellite image colorization is a broad challenging problem in the domain of remote sensing (RS) having huge potential applications. The problem becomes even more complicated under the few-shot setting yet it has barely been studied to date. In this paper, we propose a colorization framework for the RS scene for synthesizing optical images from their panchromatic (PAN) counterparts using color injection and attention fusion mechanism. Our proposed model ensures that the synthesized optical images are coherent with the structural variability of panchromatic images while constraining the realistic appearance in the optical domain from a few training image pairs. To accomplish the same, we introduce a novel Dual Attention fusion of Receptive Kernels (DARK) which considers the spatial nuances along with color injection conditioned on prior label allocation. DARK is a multi spectral-spatial feature generator that selectively accentuates important cross-spatial features based on attention fusion. We also employ a prior distribution constraint on color embedding generation for introducing vibrant yet diverse variance in a color generation. Our approach achieves state-of-the-art results on the publicly available EuroSAT and PatternNet datasets while demonstrating significant speedups. We showcase our results quantitatively by comparing the PSNR, mean squared error(MSE), and cosine similarity of generated images and qualitatively via visual perception.