标题: A Deep Learning Method for Land Use Classification Based on Feature Augmentation
作者: Wang, Y (Wang, Yue); Zhang, WS (Zhang, Wanshun); Liu, X (Liu, Xin); Peng, H (Peng, Hong); Lin, MB (Lin, Minbo); Li, A (Li, Ao); Jiang, AN (Jiang, Anna); Ma, N (Ma, Ning); Wang, L (Wang, Lu)
来源出版物: REMOTE SENSING 卷: 17 期: 8 文献号: 1398 DOI: 10.3390/rs17081398 Published Date: 2025 APR 14
摘要: Land use monitoring by satellite remote sensing can improve the capacity of ecosystem resources management. The satellite source, bandwidth, computing speed, data storage and cost constrain the development and application in the field. A novel deep learning classification method based on feature augmentation (CNNs-FA) is developed in this paper, which offers a robust avenue to realize regional low-cost and high-precision land use monitoring. Twenty-two spectral indices are integrated to augment vegetation, soil and water features, which are used for convolutional neural networks (CNNs) learning to effectively differentiate seven land use types, including cropland, forest, grass, built-up, bare, wetland and water. Results indicated that multiple spectral indices can effectively distinguish land uses with a similar reflectance, achieving an overall accuracy of 99.70%, 94.81% and 90.07%, respectively, and a kappa coefficient of 99.96%, 98.62% and 99.76%, respectively, for Bayannur, Ordos and the Hong Lake Basin (HLB). The overall accuracy of 98.18% for the field investigation demonstrated that the accuracy of the classification in wet areas and ecologically sensitive areas was characterized by significant desert-grassland interspersion.
作者关键词: land use; feature augmentation; CNNs; remote sensing; spectral indices
KeyWords Plus: CONVOLUTIONAL NEURAL-NETWORKS; COVER CLASSIFICATION; WATER INDEX; VEGETATION; CNN; CHINA; NDVI; CORN
地址: [Wang, Yue; Zhang, Wanshun; Liu, Xin; Lin, Minbo; Li, Ao; Jiang, Anna] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
[Zhang, Wanshun] Wuhan Univ, China Inst Dev Strategy & Planning, Wuhan 430079, Peoples R China.
[Zhang, Wanshun] Wuhan Univ, State Key Lab Water Resources Hydropower Engn Sci, Wuhan 430072, Peoples R China.
[Peng, Hong] Wuhan Univ, Sch Water Resources & Hydropower Engn, Wuhan 430072, Peoples R China.
[Ma, Ning; Wang, Lu] Inner Mongolia Civil Mil Integrat Dev Res Ctr, Hohhot 010070, Peoples R China.
通讯作者地址: Zhang, WS (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
Zhang, WS (通讯作者)，Wuhan Univ, China Inst Dev Strategy & Planning, Wuhan 430079, Peoples R China.
Zhang, WS (通讯作者)，Wuhan Univ, State Key Lab Water Resources Hydropower Engn Sci, Wuhan 430072, Peoples R China.
电子邮件地址: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
影响因子：4.2
