Citation: Zhang, H.; Rao, P.; Chen, X.;
Xia, H.; Zhang, S. Denoising and
Feature Extraction for Space Infrared
Dim Target Recognition Utilizing
Optimal VMD and Dual-Band
Thermometry. Machines 2022, 10, 168.
https://doi.org/10.3390/
machines10030168
Academic Editor: Kelvin K.L. Wong
Received: 17 January 2022
Accepted: 21 February 2022
Published: 23 February 2022
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Article
Denoising and Feature Extraction for Space Infrared Dim
Target Recognition Utilizing Optimal VMD and
Dual-Band Thermometry
Hao Zhang
1,2,3
, Peng Rao
1,2,
*, Xin Chen
1,2
, Hui Xia
1,2
and Shenghao Zhang
1,2,3
1
Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China;
zhanghao@mail.sitp.ac.cn (H.Z.); chenxin@mail.sitp.ac.cn (X.C.); xiahui@mail.sitp.ac.cn (H.X.);
zhangshenghao@mail.sitp.ac.cn (S.Z.)
2
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
* Correspondence: peng_rao@mail.sitp.ac.cn
Abstract:
Space target feature extraction and space infrared target recognition are important com-
ponents of space situational awareness (SSA). However, owing to far imaging distance between the
space target and infrared detector, the infrared signal of the target received by the detector is dim
and easily contaminated by noise. To effectively improve the accuracy of feature extraction and
recognition, it is essential to suppress the noise of the infrared signal. Hence, a novel denoising
and extracting feature method combinating optimal variational mode decomposition (VMD) and
dual-band thermometry (DBT) is proposed. It takes the mean weighted fuzzy-distribution entropy
(FuzzDistEn) of the band-limited intrinsic mode functions (BLIMFs) as the optimization index of
dragonfly algorithm (DA) to obtain the optimal parameters (K,
α
) of VMD. Then the VMD is utilized
to decompose the noisy signal to obtain a series of BLIMFs and the Pearson correlation coefficient
(PCC) is proposed to determine the effective modes to reconstructe the denoising signal. Finally,
based on the denoising signal, the feature of temperature and emissivity-area product are calculated
using the DBT. The simulation and experiment results show that the proposed method has better
noise reduction performance compared with the other denoising methods, and the accuracy of feature
extraction is improved at different noise equivalent irradiance. This provides more accurate feature
of temerpature and emissivity-area product for space infrared dim target recognition.
Keywords:
infrared signal; denoising; feature extraction; variational mode decomposition; dragonfly
algorithm; dual-band thermometry; temperature; emissivity-area product
1. Introduction
The research on the infrared radiation characteristics of space targets has attracted
extensive attention in many application fields, such as space kill assessment (SKA), space
debris monitoring and removal, dim target detection, etc. [
1
–
3
]. Infrared radiation is not
only an important information source for target detection, and recognition, but also an
important basis for infrared system design and experiment [
4
]. Due to the fact that the
far imaging distance between the space target and infrared detector, the target usually
emerges as only one or several pixels on the infrared image, the infrared signal of the
target is dim, and information of target such as shape, attitude, and texture is missing [
5
,
6
].
All the information, such as the temperature, emissivity-area product, micromotion, is
contained in a few pixels. This brings the possibility of feature extraction, classification, and
recognition of space dim targets. However, the factors of the detector system, including
temperature variation effect, calibration error of sensor location, photoelectrical noise,
seriously interferes with the infrared radiation signal of the space target, resulting in a
low signal-to-noise ratio (SNR) of the signal. It restricts the classification and recognition
Machines 2022, 10, 168. https://doi.org/10.3390/machines10030168 https://www.mdpi.com/journal/machines
