Name
Abstract | ||
|---|---|---|
Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12172669 | REMOTE SENSING |
Keywords | DocType | Volume |
change detection,convolutional neural networks,deep learning | Journal | 12 |
Issue | Citations | PageRank |
17 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhao Qian | 1 | 0 | 0.34 |
| Min Xia | 2 | 52 | 6.70 |
| Yonghong Zhang | 3 | 7 | 3.89 |
| Jia Liu | 4 | 72 | 21.41 |
| Yiqing Xu | 5 | 23 | 4.11 |