TY - GEN
T1 - Hyperspectral Target Detection by Fractional Fourier Transform
AU - Zhao, Xiaobin
AU - Li, Wei
AU - Shan, Tao
AU - Li, Lu
AU - Tao, Ran
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Target detection in hyperspectral images (HSI) is an important technique and many target detection algorithms have been developed in recent years. The most widely detection algorithms by the original spectral characteristics may lack the ability of target signal enhancement and background suppression. This paper presents an efficient algorithm for detecting hyperspectral targets based on fractional Fourier transform (FrFT). Firstly, fractional Fourier transform primary search is used as preprocessing to obtain the better intermediate domain features with complementary characteristics between the original reflection spectrum and the Fourier transform domain. Secondly, fractional Fourier transform secondary search and constrained energy minimization (FrFT-CEM) was adopted to find an optimal fractional order to distinguish the target from the background. The proposed method has been proved to be superior in two real hyperspectral data sets.
AB - Target detection in hyperspectral images (HSI) is an important technique and many target detection algorithms have been developed in recent years. The most widely detection algorithms by the original spectral characteristics may lack the ability of target signal enhancement and background suppression. This paper presents an efficient algorithm for detecting hyperspectral targets based on fractional Fourier transform (FrFT). Firstly, fractional Fourier transform primary search is used as preprocessing to obtain the better intermediate domain features with complementary characteristics between the original reflection spectrum and the Fourier transform domain. Secondly, fractional Fourier transform secondary search and constrained energy minimization (FrFT-CEM) was adopted to find an optimal fractional order to distinguish the target from the background. The proposed method has been proved to be superior in two real hyperspectral data sets.
KW - Hyperspectral imaging (HSI)
KW - constrained energy minimization (CEM)
KW - fractional Fourier transform (FrFT)
KW - target detection
UR - http://www.scopus.com/inward/record.url?scp=85101967998&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9323373
DO - 10.1109/IGARSS39084.2020.9323373
M3 - Conference contribution
AN - SCOPUS:85101967998
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1655
EP - 1658
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -