| Autor: |
Ruochen Liu, Zhihong Cheng, Langlang Zhang, Jianxia Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 7, Pp 156349-156359 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2947286 |
| Popis: |
Change detection is one of the core issues of earth observation and has been extensively studied in recent decades. This paper presents a novel deep neural network architecture based on information transmission and attention mechanism. Existing methods rely on a simple mechanism for independently encoding bi-temporal images to obtain their representation vectors. In view of the fact that these methods do not make full use of the rich information between bi-temporal images, we introduce the information transmission module in the design of DNN structure for doing the transmission and interaction of information. In addition, we introduce the attention mechanism behind the information transmission module to give the corresponding attention weight to each temporal image feature so as to enhance the change information of the image, which noticeably improves final prediction. The proposed network is validated on real remote sensing image data sets. Both visual and quantitative analyses of the experimental results demonstrate competitiveness of the proposed method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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