TY - GEN
T1 - DEEP-RX FOR HYPERSPECTRAL ANOMALY DETECTION
AU - Song, Yingjie
AU - Shi, Shuaikai
AU - Chen, Jie
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Hyperspectral anomaly detection is a widely studied topic that has garnered significant attention in recent years. However, designing effective nonlinear detectors remains a challenge for many traditional methods. To address this issue, we propose the integration of a variational autoencoder (VAE) in this paper. The VAE enables efficient feature extraction from hyperspectral images (HSIs) by mapping inputs to latent variables that follow a Gaussian distribution. The resulting latent representations are subsequently passed to the Reed-Xiaoli (RX) detector to obtain the final detection results. Through extensive testing on three real datasets, the detection results demonstrate the superiority of our proposed method.
AB - Hyperspectral anomaly detection is a widely studied topic that has garnered significant attention in recent years. However, designing effective nonlinear detectors remains a challenge for many traditional methods. To address this issue, we propose the integration of a variational autoencoder (VAE) in this paper. The VAE enables efficient feature extraction from hyperspectral images (HSIs) by mapping inputs to latent variables that follow a Gaussian distribution. The resulting latent representations are subsequently passed to the Reed-Xiaoli (RX) detector to obtain the final detection results. Through extensive testing on three real datasets, the detection results demonstrate the superiority of our proposed method.
KW - Hyperspectral anomaly detection
KW - RX detector
KW - variational autoencoder (VAE)
UR - https://www.scopus.com/pages/publications/85187955868
U2 - 10.1109/IGARSS52108.2023.10283137
DO - 10.1109/IGARSS52108.2023.10283137
M3 - 会议稿件
AN - SCOPUS:85187955868
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 7348
EP - 7351
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -