Journal Article

Land cover classification from remote sensing images based on multi-scale fully convolutional network

Zhang, Ce, Zheng, Shunyi, Wang, Libo, Duan, Chenxi, Li, Rui

2022

Overview

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 .

• Publisher: Taylor & Francis,Taylor & Francis Ltd,Taylor & Francis Group
• Subject: Ablation / Artificial neural networks / Datasets / Image classification / Information retrieval / Kernels / Land cover / land cover classification / Multi-Scale Fully Convolutional Network / Remote sensing / Satellite imagery / Spatio-temporal remote sensing images