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Comparative research on multi-algorithm of soil salinity monitoring based on Gaofen-5, Sentinel-1, and Sentinel-2

INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第15期44卷 4704-4726页

作者： Jiang, Xiaofang Duan, Hanchen Liao, Jie Guo, Pinglin Huang, Cuihua Xue, Xian Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Desert & Desertificat Lanzhou Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Drylands Salinizat Res Stn Lanzhou Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Drylands Salinizat Res Stn Lanzhou 730000 Peoples R China

Soil ecosystem provides a guarantee for the ecological environment. Soil salinization is a typical land degradation problem. In order to explore the applicability of GF-5, Sentinel-1, and Sentinel-2 remote sensing data in soil salinity research, this study selected Golmud of Qaidam Basin in China as the study area, using particle swarm optimization-artificial neural network (PSO-ANN), partial least squares regression (PLSR), and whale optimization algorithm-extreme learning machine (WOA-ELM) to predict soil salinity. The research results indicated that the correlation between Sentinel-2 and electric conductivity (EC) was higher than GF-5 and Sentinel-1. The simulation accuracy of models based on Sentinel-2 was slightly higher than that based on GF-5 and Sentinel-1. The three models' mean values of root mean square error (RMSE), R-2, and mean absolute error (MAE) of Sentinel-2 were slightly better than those of GF-5 and Sentinel-1, and the fitting accuracy of GF-5 was similar to that of Sentinel-1 on the whole. The result proved that Sentinel-1, GF-5, and Sentinel-2 were suitable for salinity monitoring because the strong penetrability and sensitivity of Sentinel-1, the high spectral resolution of GF-5, and the high spatial resolution of Sentinel-2. The performance of PSO-ANN was the best, and that of PLSR was the worst. The salinity mapping result of Sentinel-2 coupled with PSO-ANN proved that machine learning methods and spaceborne data could be used to study large-scale soil salinization.

关键词： Salinization PSO-ANN spaceborne remote sensing multi-algorithm

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An Adaptive multi-algorithm Ensemble for Fingerprint Matching 12th

An Adaptive Multi-algorithm Ensemble for Fingerprint Matchin...

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12th International Conference on Intelligent Computing (ICIC)

作者： Tiwari, Kamlesh Kaushik, Vandana Dixit Gupta, Phalguni Birla Inst Technol & Sci Dept CSIS Pilani 333031 Rajasthan India Harcourt Butler Technol Inst Dept CSE Kanpur 208002 Uttar Pradesh India Natl Inst Tech Teachers Training & Res Kolkata 700106 WB India

ISBN: (纸本)9783319422916;9783319422909

Any well known fingerprint matching algorithm cannot provide 100% accuracy for all databases. One should explore the possibility of fusion of multi-algorithms to achieve better performance on such databases. One of the major challenges is to design a fusion strategy which is both adaptive and improving with respect to the candidate database. This paper proposes an adaptive ensemble using statistical properties of two well known state-of-the-art minutiae based fingerprint matching algorithms to achieve (1) improvement on fingerprint recognition benchmark, (2) outperform on multiple databases. Experiments have been conducted on two databases containing multiple fingerprint impressions of 140 and 500 users. One of them is widely used publicly available databases and another one is our in-house database. Experimental results have shown the significant gain in performance.

关键词： Fingerprint Matching Minutiae ROC curve multi-algorithm Adaptive

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Palm Vein Recognition Based on multi-algorithm and Score-level Fusion 7

Palm Vein Recognition Based on Multi-algorithm and Score-lev...

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7th International Symposium on Computational Intelligence and Design (ISCID)

作者： Yan, Xuekui Deng, Feiqi Kang, Wenxiong South China Univ Technol Sch Automat Sci & Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479970056

In order to improve the recognition rate of palm vein recognition algorithm, a recognition algorithm based on SIFT and ORB features extraction and score-level fusion is presented in this paper. Score-level fusion is an information fusion technique, which has six common combination rules. Two dynamic weight combination rules are proposed as a supplement here. The main steps of the proposed algorithm are: First, extract Region of interest (ROI) from the registered palm vein image and the to-be-matched palm vein image and process them with sharpen enhancement, and then extract SIFT features and ORB features and obtain matching scores respectively, finally utilize score-level fusion to compute the final score for decision. The experiments on the CASIA Palm Vein Image Database show that the algorithm attains the best recognition rate by utilizing the min-rule, and the equal error rate (EER) is 0.36%.

关键词： palm vein recognition multi-algorithm SIFT ORB score-level fusion combination rule

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A hybrid multimodal biometric scheme based on face and both irises integration for person authentication

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2021年第3期36卷 270-292页

作者： Nouar, Larbi Taleb, Nasreddine El-Mezouar, Miloud Chikr Univ Djillali Liabes Sidi Bel Abbes Dept Elect RCAM Lab Sidi Bel Abbes 22000 Algeria

In the biometric field, mono-modal biometry suffers from multiple lacks. The use of multiple biometrics has helped getting over those limitations and has allowed outperforming single biometrics in terms of recognition rate. In this paper, we propose a new approach that fuses the Gabor-Winger transform and the oriented Gabor phase information for feature extraction as well as a hybrid scheme consisting of multi-algorithm, multi-instance and multi-modal systems that integrates face and both left and right irises of the same subject. The SDUMLAHMT database has been used to evaluate the proposed approach. The results show that our multi-modal biometric system achieves higher accuracy than the single biometric approaches as well as the other existing multi-biometric systems.

关键词： biometrics multi-biometric systems iris recognition face recognition fusion multi-algorithm DET curve feature extraction

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A multi-algorithm Balancing Convergence and Diversity for multi-Objective Optimization

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2013年第5期29卷 811-834页

作者： Xie, Datong Ding, Lixin Hu, Yurong Wang, Shenwen Xie, ChengWang Jiang, Lei Wuhan Univ Sch Comp State Key Lab Software Engn Wuhan 430000 Peoples R China Fujian Commercial Coll Dept Informat Management Engn Fuzhou 350000 Peoples R China Shijiazhuang Univ Econ Dept Informat Engn Shijiazhuang Peoples R China East China Jiao Tong Univ Sch Software Nanchang 330000 Peoples R China Hunan Univ Sci & Technol Key Lab Knowledge Proc & Networked Manufacture Xiangtan 411201 Peoples R China

As a population-based method, evolutionary algorithms have been extensively used to solve multi-objective optimization problems. However, most of the current multi-objective evolutionary algorithms (MOEAs) cannot strike a good balance between the closeness to the true Pareto front and the uniform distribution of non-dominated solutions. In this paper, we present a multi-algorithm, MABNI, which is based on two popular MOEAs, NSGA-II and IBEA. The proposed algorithm is inspired from the strengths and weaknesses of the two algorithms, e.g., the former can preserve extreme solutions effectively but has a worse diversity while the latter shows a better convergence and makes non-dominated solutions more evenly distributed but easily suffers losses of extreme solutions. In MABNI, modified NSGA-II and IBEA run alternatively and the update principle for the archive population is based on the distances to nearest neighbors. Furthermore, accompanied with preservation of extreme points, an improved differential evolution is employed to speed the search. The performance of MABNI is examined on ZDT-series and DTLZ-series test instances in terms of the selected performance indicators. Compared with NSGA-II and IBEA, the results indicate that MABNI can reach a better balance between convergence and diversity for the approximation of the true Pareto front and obtain more stable results.

关键词： multi-algorithm multi-objective optimization evolutionary algorithm nearest neighbor extreme solution

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Assessment of automatic strategies for combining QRS detections by multiple algorithms in multiple leads

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PHYSIOLOGICAL MEASUREMENT 2019年第11期40卷 114002页

作者： Llamedo, Mariano Pablo Martinez, Juan Natl Technol Univ Elect Dept GIBIO Buenos Aires DF Argentina Univ Zaragoza IIS Aragon Inst Engn Res BSICoS Grp Zaragoza Spain CIBER BBN Zaragoza Spain

Objective: To develop and evaluate an algorithm for the selection of the best-performing QRS detections from multiple algorithms and ECG leads. Approach: The detections produced by several publicly available single-lead QRS detectors are segmented in 20 s consecutive windows;then a statistical model is trained to estimate a quality metric that is used to rank each 20 s segment of detections. The model describes each heartbeat in terms of six features calculated from the RR interval series, and one feature proportional to the number of heartbeats detected in other leads in a neighborhood of the current heartbeat. With the highest ranked segments, we defined several lead selection strategies (LSS) that were evaluated in a set of 1754 ECG recordings from 14 ECG databases. The LSS proposed were compared with simple strategies such as selecting lead II or the first lead available in a recording. The performance was calculated in terms of the average sensitivity, positive predictive value, and F score. Main results: The best-performing LSS, based on wavedet algorithm, achieved an F score of 98.7, with sensitivity S = 99.2 and positive predictive value P = 98.3. The F score for the simpler strategy using the same algorithm was 92.7. The LSS studied in this work have been made available in an open-source toolbox to ease the reproducibility and result comparison. Significance: The results suggest that the use of LSS is convenient for purposes of selecting the best heartbeat locations among those provided by different detectors in different leads, obtaining better results than any of the algorithms individually.

关键词： ECG QRS detection lead selection multilead signal processing pattern recognition multi-algorithm

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multi-algorithm Feature Level Fusion Using Finger Knuckle Print Biometric

Multi-algorithm Feature Level Fusion Using Finger Knuckle Pr...

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International Conference on Future-Generation Communication and Networking (FGCN 2012)/International Conference on Digital Contents and Applications (DCA 2012)

作者： AlMahafzah, Harbi Imran, Mohammad Sheshadri, H. S. Univ Mysore PET Fdn Res Mandya India Univ Mysore Dept Study Comp Sci Mandya India VTU PES Coll Engn Dept E&C Engg Mandya India

ISBN: (纸本)9783642355936

This paper proposed the use of multi-algorithm feature level fusion as a means to improve the performance of Finger Knuckle Print (FKP) verification. LG, LPQ, PCA, and LPP have been used to extract the FKP features. Experiments are performed using the FKP database, which consists of 7,920 images. Results indicate that the multi-algorithm verification approach outperforms higher performance than using any single algorithm. The biometric performance using feature level fusions under different normalization technique as well have been demonstrated in this paper.

关键词： Feature Level Fusion multi-Biometric multi-algorithm Normalization

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Consolidated optimization algorithm for resource-constrained project scheduling problems

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INFORMATION SCIENCES 2017年 418卷 346-362页

作者： Elsayed, Saber Sarker, Ruhul Ray, Tapabrata Coello Coello, Carlos Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT Australia IPN CINVESTAV Dept Computac Mexico City DF Mexico

Resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances evaluated using well-established test instances with various levels of complexity. While it is desirable to obtain a high-quality solution and fast rate of convergence from an optimization algorithm, no single one performs well across the entire space of instances. Furthermore, even fora given algorithm, the optimal choice of its operators and control parameters may vary from one problem to another. To deal with this issue, we present a generic framework for solving RCPSPs in which various meta-heuristics, each with multiple search operators, are self-adaptively used during the search process and more emphasis is placed on the better-performing algorithms, and their underlying search operators. To further improve the rate of convergence and introduce good-quality solutions into the population earlier, a local search approach is introduced. The experimental results clearly indicate the capability of the proposed algorithm to attain high-quality results using a small population. Compared with several state-of-the-art algorithms, the proposed one delivers the best solutions for problems with 30 and 60 activities, and is very competitive for those involving 120 activities. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Resource-constrained project scheduling problems Evolutionary algorithms multi-algorithm multi-operator

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Facial-Forensic Analysis Tool

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Digital Investigation 2018年 26卷 S136-S136页

作者： Hiba Al-Kawaz Nathan Clarke Steven Furnell Fudong Li Centre for Security Communications and Network Research University of Plymouth Plymouth UK Security Research Institute Edith Cowan University Perth Western Australia Australia Centre for Research in Information and Cyber Security Nelson Mandela Metropolitan University Port Elizabeth South Africa School of Computing University of Portsmouth Portsmouth UK

Facial recognition has played an essential role in digital forensics due to the widespread use of digital technology such as CCTV, mobile phones, and digital cameras. Therefore, the growing volume of multimedia files (photos and videos), in particular, are a valuable source of evidence and the ability to identify culprits’ is invaluable. Despite significant efforts that have been given to this area, facial recognition suffers from several drawbacks in achieving recognition. These reasons are caused by photos conditions issues such as bad illumination, facial orientation, facial expression, photo quality, accessories (e.g., hat, glasses), and aging. The Facial-Forensic Analysis Tool (F-FAT) provides a technique that aids the forensic investigation in terms of the automatic facial recognition. It is a holistic system that is developed to collect, exam, and analyse multimedia evidence (photos and videos) using a multi-algorithmic fusion approach to overcome the weaknesses in individual algorithms and achieve a better accuracy for identification. The proposed approach also helps to reduce the cognitive load placed upon the investigator by providing a variety of forensic analyses such as, geo-location, facial modification, and social networks, to enable quicker answers to queries. This tool has also been designed based upon a case management concept that helps to manage the overall system, provide robust authentication, authorization and chain of custody.

关键词： Digital forensic Face recognition multi-algorithm Fusion

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Fusion of Face Recognition Methods at Score level

Fusion of Face Recognition Methods at Score level

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International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Cherifi, Dalila Cherfaoui, Fateh Yacini, Si Nabil Nait-Ali, Amine Univ Boumerdes Inst Elect & Elect Engn Boumerdes Algeria Univ Paris EST Lab Images Signaux & Syst Intelligent F-94400 Vitry Sur Seine France

ISBN: (纸本)9781509045686

The face recognition problem has been extensively studied by many researchers but accuracy is not satisfactory. This work presents analysis and performance evaluation of global methods (PCA, FLD, DCT, DWT), local methods (SIFT, LBP) and all possible fusions of two methods among them. The fusion is done by consolidating the output of multiple feature extraction algorithms at score levels using four fusion rules which are Mean, Maximum, Minimum, and product [I]. The hybrid methods are tested in terms of recognition rate under different working environment and conditions such as pose variation, illumination and facial expression change, adding effects as blur, motion, noise or combination of those effects. Finally, their robustness is tested by adding some objects as wearing a scarf, putting on a hat, background change, wearing glasses and beard.

关键词： Face Recognition fusion multi-algorithm score levels PCA FLD DCT DWT LBP SIFT

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