Remote sensing image classification technology has been widely used in land resource monitoring, forest resource investigation, and other related fields. For the traditional classification methods, terrain effect is an unavoidable factor that restricts the improvement of classification accuracy in the classification of land cover types, and the influence can be weakened by an appropriate correction model, and topographic correction has been proven to play a positive role in improving classification accuracy. Compared with traditional classifiers, deep neural network classifiers based on deep learning theory have the advantages of deep feature learning and expression, which have emerged in the field of image classification and have been gradually applied to land cover classification with good results.Our study has some shortcomings. First, some errors remain in GlobeLand30 and national forest classification products compared with the actual surface cover types. Therefore, more detailed and accurate classification results need to be found as sample labels in the future. Second, the surface of sunny-shady slopes with different types of coverage is not considered in this study. Finally, U-Net should not be the sole focus. In subsequent research, we will attempt to select a variety of deep neural network classifiers to explore the classification accuracy changes before and after topographic correction of neural network models at a more precise scale to obtain further conclusions.This paper further explores the influence of topographic correction on the classification accuracy of land cover classification by deep neural network classifiers.Using Landsat 8 OLI image data and GDEM_V2 terrain data with 30 m resolution as data sources, and based on the classification results of GlobeLand30 and national forest types, this paper implemented the classification extraction of land cover types by the U-Net semantic segmentation network and made a comparative analysis of classification accuracy before and after topographic correction with different sample acquisition methods and different levels of classification systems.Classification results show the following. (1) With two training sample acquisition methods, namely, grid clipping and aspect auxiliary clipping, the classification accuracy after topographic correction is unchanged or slightly reduced compared with that before correction, and the reduction range is 0.9%—1.39%. (2) For the classification of more precise forest types, the classification accuracy after topographic correction decreased by 1.66% compared with that before correction.In this paper, we conduct a preliminary study and find that in the classification of land cover types by the U-Net model with different sample acquisition methods, namely, regular grid clipping and aspect auxiliary clipping, and under different classification systems, the classification accuracy of the deep neural network classifier-U-Net is not improved by the topographic correction process.Our study has some shortcomings. First, some errors remain in GlobeLand30 and national forest classification products compared with the actual surface cover types. Therefore, more detailed and accurate classification results need to be found as sample labels in the future. Second, the surface of sunny-shady slopes with different types of coverage is not considered in this study. Finally, U-Net should not be the sole focus. In subsequent research, we will attempt to select a variety of deep neural network classifiers to explore the classification accuracy changes before and after topographic correction of neural network models at a more precise scale to obtain further conclusions.

remote sensing classification;deep neural network;U-Net model;topographic correction;land cover classification;classification accuracy