Affiliation:
1. College of Civil Engineering and Architecture, Jishou University, Zhangjiajie 427000, China
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Abstract
The black soil region of Northeast China is one of the most fertile soil areas in the world and serves as a crucial grain-producing region in China. However, excessive development and improper utilization have led to severe land use issues. Conducting land cover extraction in this region can provide essential data support for monitoring and managing natural resources effectively. This article utilizes GF-6 remote sensing imagery as the data source and adopts the U-Net model as the backbone network. By incorporating residual modules and adjusting the convolution kernel size, a high-precision land cover extraction model called RAT-UNet is developed. Taking Qiqihar City as an example, the RAT-UNet model is applied to extract land cover information. The results are as follows: (1) The RAT-UNet model achieves high accuracy in land cover extraction, with the following accuracies for different land types: cropland (95.11%), forestland (93.61%), grassland (68.41%), water bodies (94.67%), residential land (89.40%), and unused land (87.25%). (2) The land cover extraction performance of the RAT-UNet model is superior to DeepLabV3, U-Net, SegNet, and LinkNet34 models. This research outcome provides methodological support for the intelligent and high-precision extraction of land cover information and also offers timely data for Qiqihar city’s land use planning.
Funder
Strategic Priority Research Program of Chinese Academy of Sciences
Hunan Provincial Postgraduate Scientific Research and Innovation Project
Subject
Nature and Landscape Conservation,Ecology,Global and Planetary Change
Reference34 articles.
1. Analysis of the Spatiotemporal Characteristics of Land Use Change in the Northeast Black Soil Region over the Past Half Century;Xiao;Geophys. Geochem. Explor.,2022
2. Shen, C. (2021). China Science Daily, 12 July 2021, China Science Daily Media Group.
3. Provincial-Scale Land Type Classification Based on the Objectives of Black Soil Protection: A Case Study of Heilongjiang Province;Liu;Sci. Geogr. Sin.,2022
4. Land-use land-cover classification by machine learning classifiers for satellite observations—A review;Talukdar;Remote Sens.,2020
5. Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery;Keshtkar;Arab. J. Geosci.,2017