太阳成8722(中国)有限公司-GREEN NO.1

标题:Time series remote sensing image classification framework using combination of deep learning and multiple classifiers system

作者: Dou, P (Dou, Peng); Shen, HF (Shen, Huanfeng); Li, ZW (Li, Zhiwei); Guan, XB (Guan, Xiaobin)

来源出版物: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 卷: 103 DOI: 10.1016/j.jag.2021.102477 出版年: DEC 1 2021

摘要: Recently, time series image (TSI) has been reported to be an effective resource to mapping fine land use/land cover (LULC), and deep learning, in particular, has been gaining growing attention in this field. However, deep learning methods using single classifier need further improvement for accurate TSI classification owing to the 1D temporal properties and insufficient dense time series of the remote sensing images. To overcome such disadvantages, we proposed an innovative approach involving construction of TSI and combination of deep learning and multiple classifiers system (MCS). Firstly, we used a normalised difference index (NDI) to establish an NDIsbased TSI and then designed a framework consisting of a deep learning-based feature extractor and multiple classifiers system (MCS) based classification model to classify the TSI. With the new approach, our experiments were conducted on Landsat images located in two counties, Sutter and Kings in California, United States. The experimental results indicate that our proposed method achieves great progress on accuracy improvement and LULC mapping, outperforming classifications using comparative deep learning and non-deep learning methods.

作者关键词:Time series image classification; Remote sensing image classification; Ensemble learning; Deep learning; Normalised differential index

地址: [Dou, Peng] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou, Peoples R China.
[Dou, Peng; Shen, Huanfeng; Li, Zhiwei; Guan, Xiaobin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

通讯作者地址: Li, ZW (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

电子邮件地址:00032042@whu.edu.cn; shenhf@whu.edu.cn; lizw@whu.edu.cn; guanxb@whu.edu.cn

影响因子：5.933