Name
Title | ||
|---|---|---|
Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy. |
Abstract | ||
|---|---|---|
Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12142292 | REMOTE SENSING |
Keywords | DocType | Volume |
remote sensing,land cover classification,spectral feature,context feature,convolutional neural networks | Journal | 12 |
Issue | Citations | PageRank |
14 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Luo | 1 | 2 | 3.76 |
| Xiaohua Tong | 2 | 254 | 56.51 |
| Zhongwen Hu | 3 | 0 | 2.70 |
| Guofeng Wu | 4 | 54 | 20.01 |