Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Geo-spatial Information Science on 07/01/2022, available online: http://www.tandfonline.com/10.1080/10095020.2021.2017237
Accepted author manuscript, 16.9 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Land cover classification from remote sensing images based on multi-scale fully convolutional network
AU - Li, Rui
AU - Zheng, Shunyi
AU - Duan, Chenxi
AU - Wang, Libo
AU - Zhang, Ce
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Geo-spatial Information Science on 07/01/2022, available online: http://www.tandfonline.com/10.1080/10095020.2021.2017237
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Although the Convolutional Neural Network (CNN) has shown great potential for land cover classification, the frequently used single-scale convolution kernel limits the scope of information extraction. Therefore, we propose a Multi-Scale Fully Convolutional Network (MSFCN) with a multi-scale convolutional kernel as well as a Channel Attention Block (CAB) and a Global Pooling Module (GPM) in this paper to exploit discriminative representations from two-dimensional (2D) satellite images. Meanwhile, to explore the ability of the proposed MSFCN for spatio-temporal images, we expand our MSFCN to three-dimension using three-dimensional (3D) CNN, capable of harnessing each land cover category’s time series interaction from the reshaped spatio-temporal remote sensing images. To verify the effectiveness of the proposed MSFCN, we conduct experiments on two spatial datasets and two spatio-temporal datasets. The proposed MSFCN achieves 60.366% on the WHDLD dataset and 75.127% on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753% and 77.156%. Extensive comparative experiments and ablation studies demonstrate the effectiveness of the proposed MSFCN. Code will be available at https://github.com/lironui/MSFCN.
AB - Although the Convolutional Neural Network (CNN) has shown great potential for land cover classification, the frequently used single-scale convolution kernel limits the scope of information extraction. Therefore, we propose a Multi-Scale Fully Convolutional Network (MSFCN) with a multi-scale convolutional kernel as well as a Channel Attention Block (CAB) and a Global Pooling Module (GPM) in this paper to exploit discriminative representations from two-dimensional (2D) satellite images. Meanwhile, to explore the ability of the proposed MSFCN for spatio-temporal images, we expand our MSFCN to three-dimension using three-dimensional (3D) CNN, capable of harnessing each land cover category’s time series interaction from the reshaped spatio-temporal remote sensing images. To verify the effectiveness of the proposed MSFCN, we conduct experiments on two spatial datasets and two spatio-temporal datasets. The proposed MSFCN achieves 60.366% on the WHDLD dataset and 75.127% on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753% and 77.156%. Extensive comparative experiments and ablation studies demonstrate the effectiveness of the proposed MSFCN. Code will be available at https://github.com/lironui/MSFCN.
KW - Spatio-temporal remote sensing images
KW - Multi-Scale Fully Convolutional Network
KW - land cover classification
U2 - 10.1080/10095020.2021.2017237
DO - 10.1080/10095020.2021.2017237
M3 - Journal article
VL - 25
SP - 278
EP - 294
JO - Geo-spatial Information Science
JF - Geo-spatial Information Science
SN - 1009-5020
IS - 2
ER -