Constrained Energy Minimization with a DNN Detector

Xiaoli Yang, Min Zhao, Shuaikai Shi, Jie Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

The inherent spectral variability in hyperspectral images, the noise, and other factors bring difficulties to traditional detectors to separate the target and background by using linear decision boundaries. In this paper, by generalizing the classical constrained energy minimization (CEM) method, and considering the feature auto-extraction ability of deep neural networks (DNN), we propose a nonlinear detector based on semi-supervised learning (named deepCEM). This approach designs a deep neural network structure to provide a specific form of the nonlinear detector and trains the DNN model with knowledge of target spectra and unlabeled samples. Experiments performed on several hyperspectral data sets show that the proposed method performs better than other state-of-the-art methods.

源语言英语
主期刊名IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
出版商Institute of Electrical and Electronics Engineers Inc.
3283-3286
页数4
ISBN(电子版)9781665427920
DOI
出版状态已出版 - 2022
活动2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, 马来西亚
期限: 17 7月 202222 7月 2022

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)
2022-July

会议

会议2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
国家/地区马来西亚
Kuala Lumpur
时期17/07/2222/07/22

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