Split-Attention Networks with Self-Calibrated Convolution for Moon Impact Crater Detection from Multi-Source Data
| Autor: | Lei Liu, Rixin Yang, Gang Wan, Ying Wang, Yitian Wu, Yutong Jia, Jue Wang, Naiyang Xue |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science self-calibrated convolution Science Pattern recognition Image processing crater detection algorithm (CDA) Mars Exploration Program R-FCN transfer learning split attention mechanism Feature model remote sensing Discriminative model Impact crater Feature (computer vision) Robustness (computer science) General Earth and Planetary Sciences Artificial intelligence business Network model |
| Zdroj: | Remote Sensing, Vol 13, Iss 3193, p 3193 (2021) Remote Sensing Volume 13 Issue 16 |
| ISSN: | 2072-4292 |
| Popis: | Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability. |
| Databáze: | OpenAIRE |
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