TY - GEN
T1 - Constrained Energy Minimization with a DNN Detector
AU - Yang, Xiaoli
AU - Zhao, Min
AU - Shi, Shuaikai
AU - Chen, Jie
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - deep constrained energy minimization
KW - Hyperspectral target detection
KW - nonlinear
KW - semi-supervised learning
KW - spectral variability
UR - http://www.scopus.com/inward/record.url?scp=85141897604&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9884688
DO - 10.1109/IGARSS46834.2022.9884688
M3 - 会议稿件
AN - SCOPUS:85141897604
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3283
EP - 3286
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -