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Open AccessArticle

Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

The College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
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Remote Sens. 2019, 11(23), 2831; https://doi.org/10.3390/rs11232831
Received: 25 October 2019 / Revised: 23 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies independent and identical distribution (i.i.d.); (2) the assumption of Markov random field (MRF) would fail in more complex noise scenarios. In real scenarios, the noise is actually more complicated than the MoG model. To address this problem, a method using the non-i.i.d. mixture of Gaussians (NMoG) with modified flux density (MFD) is proposed in this paper. We firstly construct a novel data structure containing spatial and temporal information with an infrared image sequence. Then, we use an NMoG model to describe the noise, which can be separated with the background via the variational Bayes algorithm. Finally, we can select the component containing true targets through the obvious difference of target and noise in an MFD maple. Extensive experiments demonstrate that the proposed method performs better in complicated noisy scenarios than the competitive approaches. View Full-Text
Keywords: infrared small-faint target detection; non-independent and identical distribution (non-i.i.d.) mixture of Gaussians; flux density; variational Bayesian infrared small-faint target detection; non-independent and identical distribution (non-i.i.d.) mixture of Gaussians; flux density; variational Bayesian
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Sun, Y.; Yang, J.; Li, M.; An, W. Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density. Remote Sens. 2019, 11, 2831.

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