Name
Title | ||
|---|---|---|
A Dynamic End-To-End Fusion Filter For Local Climate Zone Classification Using Sar And Multi-Spectrum Remote Sensing Data |
Abstract | ||
|---|---|---|
Local Climate Zone (LCZ) classification is potentially popular because of its extensive applications. Recently, data from different remote sensors including synthetic aperture radar (SAR) and multi-spectrum are employed for LCZ classification. However, different bands in SAR and multi-spectrum are difficult to fuse because of their various physical properties. In this paper, an dynamic end-to-end fusion filter is proposed. Firstly, a convolutional neural network (CNN) based dynamic filter network (DFN) is introduced to integrate different bands in SAR and multi-spectrum data, which enhances the fusion accuracy by a flexible dynamic operation. Then the filter is used for feature extraction, hence improve the performance of the classifier. The proposed method is evaluated using Sentinel-1 and Sentinel-2 dataset and the improvement of accuracy shows the superiority of the proposed dynamic data fusion approach. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9324427 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Dynamic filter network, Local Climate Zone Classification, Multi-spectrum, End-to-end, Fusion | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengming Feng | 1 | 0 | 0.34 |
| Youtian Lin | 2 | 0 | 0.34 |
| Guangjun He | 3 | 0 | 0.34 |
| GUAN Jian | 4 | 47 | 15.77 |
| Jin Wang | 5 | 0 | 0.34 |
| Huifeng Shi | 6 | 0 | 0.34 |