A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain
By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have be...
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Published in | Sensors (Basel, Switzerland) Vol. 19; no. 3; p. 567 |
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Format | Journal Article |
Language | English |
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29.01.2019
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Abstract | By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average. |
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AbstractList | By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average. |
Author | Cao, Wen-Huan Huang, Shu-Cai |
AuthorAffiliation | Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China; hsc67118@126.com |
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Author_xml | – sequence: 1 givenname: Wen-Huan surname: Cao fullname: Cao, Wen-Huan email: WHCao666@163.com organization: Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China. WHCao666@163.com – sequence: 2 givenname: Shu-Cai surname: Huang fullname: Huang, Shu-Cai email: hsc67118@126.com organization: Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China. hsc67118@126.com |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30700051$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1109/TIP.2008.924386 10.1049/el.2013.2159 10.1364/OE.26.025676 10.1109/TIT.2005.858979 10.1364/AO.56.000069 10.1109/83.392338 10.1109/TAES.2014.130076 10.1038/srep37862 |
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Keywords | adaptive threshold method compressive domain compressive subtraction two-dimensional measurement model compressive sensing |
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Title | A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain |
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