Decision fusion strategies for SAR image target recognition

Huan, Ruohong; Pan, Yun

doi:10.1049/iet-rsn.2010.0319

Cited by 55 publications

(31 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ranking based decision fusion approach is used to fuse the outputs of the classifiers. The final recognition results are attained from the output of the fuser [23,24]. The process diagram of decision fusion strategy is shown in Figure 8.…”

Section: Decision Fusion Strategymentioning

confidence: 99%

Target Recognition for Multi-Aspect Sar Images With Fusion Strategies

Huan¹,

Pan²

2013

PIER

View full text Add to dashboard Cite

Abstract-Two fusion strategies for target recognition using multiaspect synthetic aperture radar (SAR) images are presented for recognizing ground vehicles in MSTAR database. Due to radar crosssection variability, the ability to discriminate between targets varies greatly with target aspect. Multi-aspect images of a given target are used to support recognition. In this paper, two fusion strategies for target recognition using multi-aspect SAR images are proposed, which are data fusion strategy and decision fusion strategy. The recognition performance sensitivity to the number of images and the aspect separations is analyzed for those two target recognition strategies. The two strategies are also compared with each other in probability of correct classification and operating efficiency. The experimental results indicate that if we have a small number of multi-aspect images of a target and the aspect separations between those images are proper, the probability of correct classification obtained by the two proposed strategies can be advanced significantly compared with that obtained by the method using single image.

show abstract

Section: Decision Fusion Strategymentioning

confidence: 99%

Target Recognition for Multi-Aspect Sar Images With Fusion Strategies

Huan¹,

Pan²

2013

PIER

View full text Add to dashboard Cite

show abstract

“…As mentioned earlier, combinations of classifiers can be found also in other types of pattern recognition tasks. Huan and Pan (2011) use this idea to get a better decision in SAR (Synthetic Aperture Radar) image target recognition, while Pagano et al (2012) present an algorithm for a face recognition problem. Another example is the popular application of classifier integration in multi-sensor network problems (Chi-Hwa et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

Tran

Pham

Vuong

2014

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers' performances and with other integration methods to show the high quality of the proposed solution.

show abstract

“…These base-level classifiers are respectively constructed using different techniques and methods. With the development of MCF technology, MCF has been extensively applied in many areas, such as character recognition [6], multi-sensor data classification [7] and SAR ATR [8,9].…”

Section: Introductionmentioning

confidence: 99%

A hierarchical propelled fusion strategy for SAR automatic target recognition

Cui

Cao

Yang

et al. 2013

J Wireless Com Network

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) automatic target recognition (ATR) is playing a very important role in military and civil field. Much work has been done to improve the performance of SAR ATR systems. It is well-known that ensemble methods can be used for improving prediction performance. Thus recognition using multiple classifiers fusion (MCF) has become a research hotspot in SAR ATR. Most current researchers focus on the fusion methods by parallel structure. However, such parallel structure has some disadvantages, such as large time consumption, features attribution conflict and low capability on confuser recognition. A hierarchical propelled strategy for multi-classifier fusion (HPSMCF) is proposed in this paper. The proposed HPSMCF has the characters both of series and parallel structure. Features can be used more effective and the recognition efficiency can be improved by extracting features and fusing the probabilistic outputs in a hierarchical propelled way. Meanwhile, the confuser recognition can be achieved by setting thresholds for the confidence in each level. Experiments on MSTAR public data demonstrate that the proposed HPSMCF is robust for variant recognition conditions. Compared with the parallel structure, HPSMCF has better performance both on time consumption and recognition rate.

show abstract

Decision fusion strategies for SAR image target recognition

Cited by 55 publications

References 17 publications

Target Recognition for Multi-Aspect Sar Images With Fusion Strategies

Target Recognition for Multi-Aspect Sar Images With Fusion Strategies

Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

A hierarchical propelled fusion strategy for SAR automatic target recognition

Contact Info

Product

Resources

About