SAR Target Recognition with Data Fusion

Huan, Ruohong; Mao, Keji; Lei, Yanjing; Ji-ming, YU; Xia, Ming

doi:10.1109/icie.2010.101

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…The results of majority voting classification show a marked improvement over classification with a single, monostatic, observation. This confirms the expectations set by (5). Any two bistatic observations would also produce an improved probability of correct classification for the consensus, , and would increase even more if the bistatic observations were chosen to maximize the individual probabilities of correct classification, .…”

Section: Classsupporting

confidence: 75%

“…Another branch of SAR ATR research has led to the use of aspect diversity for improving target recognition [5][6][7][8]. The approaches fuse monostatic SAR observations of a target from multiple look angles to provide more information about the target and thereby improve recognition.…”

Section: Introductionmentioning

confidence: 99%

“…The approaches fuse monostatic SAR observations of a target from multiple look angles to provide more information about the target and thereby improve recognition. Ruohong et al [5] have investigated data-level fusion using principal components analysis (PCA) and discrete wavelet transforms (DWT) to fuse multi-aspect target data. Jin et al [6] also investigated the use of PCA for data-level fusion, but noted that if the correlation between input images is low, that PCA will not preserve the fine details of the target.…”

Section: Introductionmentioning

confidence: 99%

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Bistatic aspect diversity for improved SAR target recognition

Laubie

Rigling

Penno

2015

2015 IEEE Radar Conference (RadarCon)

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-This paper analyzes the potential for improvement in the performance of automatic target recognition (ATR) for synthetic-aperture radar (SAR) with bistatic aspect diversity. Initial assessments using decisionlevel fusion of monostatic observations with bistatic observations provide promising results. Data was generated using three civilian vehicle facet files and an electromagnetic scattering simulator. Classification was performed using normalized cross-correlation template matching and majority voting. Results showed an increase in the probability of correct classification with decision-level fusion of bistatic observations over classification using single observations.

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Section: Classsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bistatic aspect diversity for improved SAR target recognition

Laubie

Rigling

Penno

2015

2015 IEEE Radar Conference (RadarCon)

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“…In the realm of automatic target recognition using synthetic aperture radar images, many recent papers describe how to improve the performance of target recognition using multiple looks (Ding and Wen, 2017;Huan and Pan, 2013;Laubie et al, 2015Laubie et al, , 2018Salvador, 2016;Situ et al, 2016;Zhang et al, 2012). Earlier research (Bhanu and Jones, 2002;Brown, 2003;Jin et al, 2006;Laine and Bauer, 2008;Ruohong et al, 2010;Snyder and Ettinger, 2003;Vespe et al, 2005). This research shows that the performance of target recognition can be significantly improved through the data fusion of observations at different looks (referred to as aspect diversity).…”

Section: Introductionmentioning

confidence: 92%

An analytical and probabilistic model with concordance for detecting mine-like objects with mirror symmetry

Nguyen

2021

JDAL

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PurposeThe purpose is to develop search and detection strategies that maximize the probability of detection of mine-like objects.Design/methodology/approachThe author have developed a methodology that incorporates variational calculus, number theory and algebra to derive a globally optimal strategy that maximizes the expected probability of detection.FindingsThe author found a set of look angles that globally maximize the probability of detection for a general class of mirror symmetric targets.Research limitations/implicationsThe optimal strategies only maximize the probability of detection and not the probability of identification.Practical implicationsIn the context of a search and detection operation, there is only a limited time to find the target before life is lost; hence, improving the chance of detection will in real terms be translated into the difference between success or failure, life or death. This rich field of study can be applied to mine countermeasure operations to make sure that the areas of operations are free of mines so that naval operations can be conducted safely.Originality/valueThere are two novel elements in this paper. First, the author determine the set of globally optimal look angles that maximize the probability of detection. Second, the author introduce the phenomenon of concordance between sensor images.

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“…6, we show PCC lines obtained by using DWT data fusion algorithm, PCA data fusion algorithm, and POCS super-resolution reconstruction algorithm in the data level with four multi-aspect images in aspect separation ranging from 1°to 30°. The step of POCS superresolution reconstruction is substituted by DWT data fusion or PCA data fusion, when DWT data fusion algorithm or PCA data fusion algorithm is used in SAR multi-aspect image recognition [9,10]. From Fig.…”

Section: Experiments and Analysis With Mstar Datasetmentioning

confidence: 99%

New structure for multi-aspect SAR image target recognition with multi-level joint consideration

Huan

Wang

Pan

et al. 2015

Multimed Tools Appl

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A new structure for SAR target recognition using multi-aspect SAR images is proposed. The new structure applies new approaches in the two levels of SAR image target recognition, which are data level and decision level, and combines the benefits of the two. Projections onto convex sets (POCS) super-resolution reconstruction algorithm is used in the data level of the new structure, which is to advance the resolution of the SAR image by using a series of multi-aspect SAR images. Weighted Bayes decision fusion algorithm is proposed in the decision level to jointly consider the benefit from the data level. All outcomes from the classifiers are fused in the decision level to generate the final result, which combines the multilevel benefits. Verification and analysis is performed to the proposed structure with multitarget image data in MSTAR database. Experimental results indicate that using the proposed structure for multi-aspect SAR images with multi-level joint consideration, the recognition rate is significantly advanced than that using single SAR target image. Meanwhile, the recognition rate by this structure is also higher than that using individual level approach.

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SAR Target Recognition with Data Fusion

Cited by 12 publications

References 9 publications

Bistatic aspect diversity for improved SAR target recognition

Bistatic aspect diversity for improved SAR target recognition

An analytical and probabilistic model with concordance for detecting mine-like objects with mirror symmetry

New structure for multi-aspect SAR image target recognition with multi-level joint consideration

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