SECBNet: Semantic Segmentation-Enhanced Color Balance Network for Optical Satellite Images

Earth observation satellites can capture optical images under different temporal, climatic conditions, and platforms exhibit substantial differences in color and brightness, leading to poor visual experiences when synthesizing large-area optical satellite images. The related issue of color balancing has attracted considerable attention from researchers, yet challenges such as a lack of research data and sensitivity to model parameters persist. To address these problems, this article publishes a publicly open dataset and presents a semantic segmentation-enhanced color balance network (SECBNet). First, to mitigate the scarcity of research data, we develop a publicly available remote sensing image color balance dataset, Zhu Hai color balance image (ZHCBI), to support related research activities. Second, to improve semantic consistency between the color-balanced images and the target images, we design a dual-branch U-Net architecture guided by segmentation results and propose a novel segmentation feature loss function. Finally, to address issues of seams and unnatural transitions between blocks in segmented processing, we introduce a postprocessing module based on weighted averaging. We conducted comparative experiments and analyses with existing mainstream color balancing algorithms on the ZHCBI dataset. The results demonstrate that our proposed method achieves state-of-the-art color balancing quality, with significant improvement in visual effects and a higher peak signal-to-noise ratio (PSNR) (23.64 dB) compared with other mainstream methods.

I. Introduction

Due to differences in lighting, climate, and imaging platforms, satellite images obtained at different times and from different sources often exhibit significant color discrepancies. These inconsistencies not only lead to poor visual integration but also adversely affect subsequent tasks such as satellite map production [1], [2], road detection [3], [4], and semantic segmentation [5], [6]. The study of color balancing techniques has thus garnered significant attention from scholars [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Current methods for color balancing in remote sensing images can be divided into traditional nondeep learning methods and those based on deep learning.