| Paper ID | SAM-9.6 |
| Paper Title |
Differential Convolution Feature Guided Deep Multi-scale Multiple Instance Learning for Aerial Scene Classification |
| Authors |
Beichen Zhou, Jingjun Yi, Qi Bi, Wuhan 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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Aerial image classification is challenging for current deep learning models due to the varied geo-spatial object scales and the complicated scene spatial arrangement. Thus, it is necessary to stress the key local feature response from a variety of scales so as to represent discriminative convolutional features. In this paper, we propose a deep multi-scale multiple instance learning (DMSMIL) framework to tackle the above challenges. Firstly, we develop a differential multi-scale dilated convolution feature extractor to exploit the different patterns from different scales. Then, the deep features of each scale are fed into a multiple instance learning module to generate a bag-level probability prediction. Lastly, probability predictions from all the MIL branches are fused to generate the final semantic prediction. Extensive experiments on three widely-utilized aerial scene classification benchmarks demonstrate that our proposed DMSMIL outperforms the state-of-the-art approaches by a large margin. |
