Name
Title | ||
|---|---|---|
Fusing Multiseasonal Sentinel-2 Imagery for Urban Land Cover Classification With Multibranch Residual Convolutional Neural Networks |
Abstract | ||
|---|---|---|
Exploiting multitemporal Sentinel-2 images for urban land cover classification has become an important research topic, since these images have become globally available at relatively fine temporal resolution, thus offering great potential for large-scale land cover mapping. However, appropriate exploitation of the images needs to address problems such as cloud cover inherent to optical satellite imagery. To this end, we propose a simple yet effective decision-level fusion approach for urban land cover prediction from multiseasonal Sentinel-2 images, using the state-of-the-art residual convolutional neural networks (ResNet). We extensively tested the approach in a cross-validation manner over a seven-city study area in central Europe. Both quantitative and qualitative results demonstrated the superior performance of the proposed fusion approach over several baseline approaches, including observation- and feature-level fusion. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LGRS.2019.2953497 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
Urban areas,Earth,Convolutional neural networks,Satellites,Europe,Remote sensing,Task analysis | Journal | 17 |
Issue | ISSN | Citations |
10 | 1545-598X | 2 |
PageRank | References | Authors |
0.40 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunping Qiu | 1 | 6 | 1.85 |
| Lichao Mou | 2 | 254 | 25.35 |
| Michael Schmitt | 3 | 59 | 12.37 |
| Xiao Xiang Zhu | 4 | 30 | 8.56 |