2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SAM-9.5 | ||
| Paper Title | A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REMOTE SENSING SCENE | ||
| Authors | Pei Zhang, Yunpeng Bai, Dong Wang, Northwestern Polytechnical University, China; Bendu Bai, Xi’an University of Posts and Telecommunications, China; Ying Li, Northwestern Polytechnical University, China | ||
| Session | SAM-9: Detection and Classification | ||
| Location | Gather.Town | ||
| Session Time: | Thursday, 10 June, 16:30 - 17:15 | ||
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 | ||
| Presentation | Poster | ||
| Topic | Sensor Array and Multichannel Signal Processing: [RAS-DTCL] Target detection, classification, localization | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | While achieving remarkable success in remote sensing (RS) scene classification for the past few years, CNN-based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a meta-learning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. | ||