期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 16, 期 9, 页码 1472-1476出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2897652
关键词
Gradient-based method; lifelong few-shot learning; meta-learning; remote sensing image; scene-classification
类别
资金
- National Natural Science Foundation [61621136008, U1613212]
The development of visual sensing technologies has made it possible to obtain some high resolution and to gather many high-resolution satellite images. To make the best use of these images, it is essential to be able to recognize and retrieve their intrinsic scene information. The problem of scene recognition in remote sensing images has recently aroused considerable interest, mainly due to the great success achieved by deep learning methods in generic image classification. Nevertheless, such methods usually require large amounts of labeled data. By contrast, remote sensing images are relatively scarce and expensive to obtain. Moreover, data sets from different aerospace research institutions exhibit large disparities. In order to address these problems, we propose a model based on a meta-learning method with the ability of learning a classifier from just few-shot samples. With the proposed model, the knowledge learned from one data set can be easily adapted to a new data set, which, in turn, would serve in the lifelong few-shot learning. Scene-level image recognition experiments, on public high-resolution remote sensing image data sets, validate our proposed lifelong few-shot learning model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据