MPSPNet和UNet网络下山东省高分辨耕地遥感提取

The rapid development of remote sensing image technology enables a large number of high-resolution remote sensing images to provide good data support for the accurate extraction of cropland and other ground features. However, high-resolution remote sensing images have large data volume and complex features, the artificial visual interpretation and traditional classification methods have limited extraction capabilities which cannot realized large-scale high-precision cropland extraction automatically. Deep learning technology has shown superior performance in the automatic extraction of remote sensing image information due to its strong ability to express features, providing a new idea for the automatic extraction of large-scale cropland. Exploring the application of different typical network models in the extraction of cropland with different landscape features is of great significance to the improvement of the quality and efficiency of cropland extraction. Based on above, the study uses the 2 m resolution data fused with GF-1 and GF-2 in 2015—2017 as the data source. Using Modified Pyramid Scene Parsing Network (MPSPNet) and UNet models applied to the fine automatic extraction of cropland in Shandong Province, and compared with the traditional object-oriented method, exploring the applicability of two deep convolutional neural network models in the automatic extraction of large-scale cropland. We also apply the trained models to the images of different regions and different time phases for the extraction of cropland, and explore the generalization ability of the models. The landscape features of cropland and uncertainty results are analyzed to explore the factors affecting the accuracy of cropland extraction by the models. Results show that: (1) MPSPNet and UNet models perform better than traditional object-oriented classification methods in the extraction of cropland at the district/county scale, the overall accuracy of the extraction of cropland at the provincial scale is better than 90% and there is no obvious difference between two models. (2) The landscape characteristic of cropland is an important factor that affects the effect of the two models, and the choice of the model has no obvious influence on the cropland extraction effect. The extraction effect is better in areas where the cropland landscape index is low and the plots are regular and flat, and the extraction effect is poor in the broken hilly areas of the plots with high cropland landscape index and in the noncropland plots whose characteristics are similar to the cropland, the UNet model is more likely to misclassify cropland in these areas. (3) The two models can obtain better cropland extraction effects in images of different regions and different time phases, and have strong generalization capabilities and temporal and spatial migration capabilities. This study proves the powerful feature learning capabilities of MPSPNet and UNET network models for high-resolution images, and the application potential of deep learning algorithms in fully automatic high-resolution cropland extraction. © 2023 National Remote Sensing Bulletin. All rights reserved.