A Nerf-Based Color Consistency Method for Remote Sensing Images

TL;DR

This paper introduces a NeRF-based approach to improve color consistency in remote sensing images affected by seasonal and atmospheric variations, enhancing image stitching quality.

Contribution

The paper presents a novel NeRF-based method for color correction in multi-view remote sensing images, addressing limitations of traditional radiation normalization techniques.

Findings

Generated images show smooth color transitions and high visual quality.

Method effectively reduces seams in stitched satellite and UAV images.

Experimental results demonstrate improved color consistency across diverse conditions.

Abstract

Due to different seasons, illumination, and atmospheric conditions, the photometric of the acquired image varies greatly, which leads to obvious stitching seams at the edges of the mosaic image. Traditional methods can be divided into two categories, one is absolute radiation correction and the other is relative radiation normalization. We propose a NeRF-based method of color consistency correction for multi-view images, which weaves image features together using implicit expressions, and then re-illuminates feature space to generate a fusion image with a new perspective. We chose Superview-1 satellite images and UAV images with large range and time difference for the experiment. Experimental results show that the synthesize image generated by our method has excellent visual effect and smooth color transition at the edges.