TY - GEN
T1 - INFRARED SMALL-TARGET DETECTION BASED ON THREE-ORDER TENSOR CREATION AND TUCKER DECOMPOSITION
AU - Zhao, Mingjing
AU - Li, Wei
AU - Li, Lu
AU - Tao, Ran
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Robust infrared small-target detection has always been a research hotspot in target search and tracking systems. However, the image itself has low signal-to-noise ratio (SNR), and the targets usually lack detailed/texture information. In addition, the background is complex and diverse. All the above factors make it easy for the targets to be submerged. In this paper, a novel method is proposed based on a three-order creation and the Tucker decomposition. First, the morphological profiles (i.e., area attribute and height attribute of max-tree) are applied to create a three-order tensor, which compensates for the lack of detailed information by supplementing spatial information in infrared images. Then, the Tucker decomposition is employed on the created tensor, in which most of the background can be estimated and eliminated from three dimensions. Finally, the target is detected on the remaining and the results of diverse morphological profiles are fused, which further enhances the target information. Experimental results demonstrate the effectiveness of the proposed method.
AB - Robust infrared small-target detection has always been a research hotspot in target search and tracking systems. However, the image itself has low signal-to-noise ratio (SNR), and the targets usually lack detailed/texture information. In addition, the background is complex and diverse. All the above factors make it easy for the targets to be submerged. In this paper, a novel method is proposed based on a three-order creation and the Tucker decomposition. First, the morphological profiles (i.e., area attribute and height attribute of max-tree) are applied to create a three-order tensor, which compensates for the lack of detailed information by supplementing spatial information in infrared images. Then, the Tucker decomposition is employed on the created tensor, in which most of the background can be estimated and eliminated from three dimensions. Finally, the target is detected on the remaining and the results of diverse morphological profiles are fused, which further enhances the target information. Experimental results demonstrate the effectiveness of the proposed method.
KW - Infrared image
KW - Morphological profiles
KW - Small-target detection
KW - Three-order tensor creation
KW - Tucker decomposition
UR - http://www.scopus.com/inward/record.url?scp=85126061903&partnerID=8YFLogxK
U2 - 10.1109/IGARSS47720.2021.9553311
DO - 10.1109/IGARSS47720.2021.9553311
M3 - Conference contribution
AN - SCOPUS:85126061903
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3129
EP - 3132
BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -