Abstract
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.
Original language | English |
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Pages (from-to) | 278-294 |
Number of pages | 17 |
Journal | Geo-spatial information science |
Volume | 25 |
Issue number | 2 |
Early online date | 7 Jan 2022 |
DOIs | |
Publication status | Published - 3 Apr 2022 |
Keywords
- Land cover classification
- Multi-Scale Fully Convolutional Network
- Spatio-temporal remote sensing images
- ITC-GOLD
- ITC-ISI-JOURNAL-ARTICLE
- UT-Gold-D