@inproceedings{76d74c1b00c54e50bf7d2133473fd08f,
title = "An Introspective Learning Strategy for Remote Sensing Scene Classification",
abstract = "In this paper, a novel introspective learning strategy for remote sensing scene classification is proposed. Through this strategy, the neural network used for classification can introspectively generate negative samples. In most training deep neural networks, negative samples are rarely noticed. We are the first to actively introduce negative samples into the remote sensing scene classification tasks. The goal of this paper is to analyze the effect of introspective negative samples on remote sensing scene classification tasks. Experiments demonstrate that the introduction of negative samples in training can effectively improve the classification accuracy and robustness. In addition, we found that our method can effectively against invalid remote sensing images.",
keywords = "Deep learning, Introspective strategie, Negative samples, Remote sensing, Scene classification",
author = "Jingran Su and Qi Wang and Shangdong Chen and Xuelong Li",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898925",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "533--536",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
}