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Attention-based DenseNet network for multi-source remote sensing classification
| Content Provider | Scilit |
|---|---|
| Author | Zhang, Lishuo Lin, Hong Zeng, Fanyang |
| Copyright Year | 2021 |
| Description | Journal: Iop Conference Series: Earth and Environmental Science Remote sensing data are abundant in spatial, image information and widely applied in earth observation covering military and civil fields. As the hot spot and critical point, scene classification has attracted increasing attention. Convolutional neural networks (CNNs) behave as representative technologies in scene classification. Nevertheless, many CNNs are lack of the ability to distinguish key information from redundant information. To overcome the problem and achieve efficient feature extractions from multi-source data, we integrate DenseNet and attention mechanism into a CNN-based network. Multi-scale convolution and threedimensional convolution are applied to extract features; and then, to explore and obtain feature information in a deeper level, a Dense structure are utilized; Afterwards, the features extracted from previous procedures are weighed due to importance and merged through the convolutional attention model. The experiment validates that the presented method poses advantages of efficiently merging various feature by Dense structure and the convolutional attention model. |
| Related Links | https://iopscience.iop.org/article/10.1088/1755-1315/865/1/012002/pdf |
| ISSN | 17551307 |
| e-ISSN | 17551315 |
| DOI | 10.1088/1755-1315/865/1/012002 |
| Journal | Iop Conference Series: Earth and Environmental Science |
| Issue Number | 1 |
| Volume Number | 865 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-10-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Earth and Environmental Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Physics and Astronomy Environmental Science |
