Scene Classification of High Resolution Remote Sensing Images Via Self-Paced Deep Learning

科研成果: 书/报告/会议事项章节会议稿件同行评审

10 引用 (Scopus)

摘要

Scene classification of high resolution remote sensing (HRRS) images is a fundamental yet challenging problem for remote sensing image analysis. In this paper, we focus on tackling the problem of HRSS scene classification using a small pool of unlabeled images and only a few labeled images per category, namely, few-shot scene classification (FSSC), which is more challenging than common scene classification task. The key challenge arises from selecting trustworthy samples from the pool of unlabeled images that have high confidence. To address this challenge, a novel local manifold constrained self-paced deep learning method is proposed. Specifically, the model is learned by gradually selecting easy samples from the pool of unlabeled images, assigning them with pseudo-labels and further adopting them with labeled images as the new training set. In addition, a local manifold constraint is introduced to enforce that the pseudo-labels assigned by the initial model should be consistent with the local manifold of the labeled samples. In such way, the confidence of the selecting samples is increased and is beneficial to train more robust classifier. Experimental results on a publicly available large scale NWPU-RESISC45 data set demonstrated the effectiveness of our method in achieving competitive performance while significantly reducing manually labeled cost.

源语言英语
主期刊名2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
521-524
页数4
ISBN(电子版)9781538691540
DOI
出版状态已出版 - 7月 2019
活动39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, 日本
期限: 28 7月 20192 8月 2019

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)

会议

会议39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
国家/地区日本
Yokohama
时期28/07/192/08/19

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