高分五号搭载的可见光短波红外高光谱相机，能获取精细的地物光谱信息，具有十分广泛的应用前景。但高光谱卫星遥感数据往往无法避免条带噪声的干扰，进行条带去除是数据预处理中不可缺少的步骤。传统方法往往对地物的异质性与丰富的细节纹理考虑不足，导致条带不能被彻底地消除。为此，本文提出了一种顾及水陆差异的影像条带去除方法，采用水体与陆地区别统计的策略，解决条带噪声在异质区域的统计特征差异问题，并结合优化统计和一维变分滤波技术实现水陆区域参考统计特征的精确估计，最终基于矩匹配方法分别实现条带去除。实验结果表明：无论是真实实验还是模拟实验，本文提出的算法相较于传统条带去除算法，能更加稳健地去除数据条带噪声，还原地表真实辐射信息；在模拟实验中，本文算法处理结果的峰值信噪比（PSNR)达到46.58，且平均绝对误差（MAE）仅有11.56，均明显优于用于比较的3种传统算法，且算法执行效率也具备优势，能够更好地适用于高分五号大数据量的处理需求。

GF-5 satellite is an important scientific research satellite in China’s high-resolution projects. It is also the first full-spectrum hyperspectral satellite in the world to simultaneously observe the atmosphere and land. GF-5 satellite can meet the urgent needs of China’s environmental monitoring, resource exploration, disaster prevention and mitigation, and other industries. However, similar to many hyperspectral satellite data, random band noises are found in some of its imaging data, thereby reducing the quality of data to a certain extent. The large data width, high spatial resolution, rich detail texture, and heterogeneity of the terrain also establish high requirements for strip removal. In this study, a method of image strip removal considering land–water differences is proposed to robustly remove strip noise in data, restore the real radiation information of the surface, and improve the application value of the GF-5 hyperspectral data. In the proposed algorithm, we use a computationally efficient moment matching algorithm as the basic framework, with the idea of separate treatment between water and land, and combined it with an optimized statistical strategy to obtain many reliable statistical results, and then use 1D variational filtering to obtain a reliable statistical reference value. The moment matching algorithm is used to correct the water and land areas for effectively removing the complex strip noise in the image. Experiments on the L1 data of the GF-5 hyperspectral data without strip preprocessing show that the proposed method can be robustly removed compared with the traditional moment matching, histogram matching, and wavelet Fourier joint filtering methods. Stripe noise in the data, especially in complex scenes, has improved removal. Therefore, the proposed method can effectively solve the high-resolution GF-5 hyperspectral data radiation degradation caused by strip noise, improve the data quality, and utilize its advantages in resource, environment, and ecological applications. This study proposes a global moment matching method based on 1D variational filtering guidance to address the band noise in GF-5 hyperspectral image data. This algorithm is based on the moment matching algorithm and uses different statistics between water and land to overcome the unreliable statistical characteristics of the bands in heterogeneous regions. 1D variational filtering technology is used to obtain water and land regions. Domains have accurate statistical reference values. The experimental results show that the proposed method can robustly remove band noise in data, effectively solve the problem of radiation degradation caused by high-score band noise, and improve the data quality. In the next step, the abundant spectral dimension information of GF-5 hyperspectral image data is utilized to correct the image data polluted by noise and improve the strip noise removal result.