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Detecting targets in SAR images: A machine learning approach [chapter]

Qi Zhang, Zoran Duric, Ryszard S. Michalski
1997 Lecture Notes in Computer Science  
KEYWORD: Learning in vision, target detection in SAR images. Related Work Target detection in SAR images is difficult because of large amount of noise in image data.  ...  This paper describes a novel application of the MIST methodology to target detection in SAR images.  ...  Maloof for his help in this work, Dr. Kaufman for his technical support in preparing image data, Jim Mitchell for his comments.  ... 
doi:10.1007/3-540-63930-6_135 fatcat:io6vfuim5rdmxphuh4el4alkqy

Kernel regression-based background predicting method for target detection in SAR image

Yanfeng Gu, Xing Liu, Jinglong Han, Ye Zhang
2009 2009 IEEE International Geoscience and Remote Sensing Symposium  
CONCLUSION In this paper, a KR-based background prediction method is proposed for small target detection in SAR imagery.  ...  In this letter, we propose a small target detecting algorithm in SAR imagery, which predicts the background clutter using 2D kernel regression to eliminate correlative background and get remaining image  ... 
doi:10.1109/igarss.2009.5417446 dblp:conf/igarss/GuLHZ09 fatcat:2krpbpii7ndnrnsqr7kprvpgzi

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

Dong Zhu, Xueqian Wang, Yayun Cheng, Gang Li
2021 Remote Sensing  
for vessel targets, compared to commonly used image fusion approaches.  ...  We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection.  ...  Classical constant false alarm rate (CFAR)-based detection approaches easily lead to missed detections for such vessel targets, especially in high-resolution SAR images [26, 27] .  ... 
doi:10.3390/rs13193957 fatcat:ggjjcrmwxnds7cxrxdshnvhrra

Target detection and classification in SAR images using region covariance and co-difference

Kaan Duman, Abdulkadir Eryildirim, A. Enis Cetin, Edmund G. Zelnio, Frederick D. Garber
2009 Algorithms for Synthetic Aperture Radar Imagery XVI  
In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed.  ...  The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification.  ...  SVM is a supervised machine learning method based on the statistical learning theory and approach developed by Vladimir Vapnik.  ... 
doi:10.1117/12.818725 fatcat:gb5r53jomjhmtl3ne4gsg6lgu4

A Novel Approach to Adopt Explainable Artificial Intelligence in X-ray Image Classification

2022 Advances in Machine Learning & Artificial Intelligence  
We apply the proposed method on a real-world application in Pneumonia Chest X-ray Image data set and produced state- of-the-art results.  ...  In view of the above needs, this study proposes an interaction- based methodology – Influence Score (I-score) – to screen out the noisy and non-informative variables in the images hence it nourishes an  ...  to much broader impact in fields of pattern recognition, computer vision, and representation learning.  ... 
doi:10.33140/amlai.03.01.01 fatcat:gtccjjy76jhmziej52oythsg7e

Assessing mangrove deforestation using pixel-based image: a machine learning approach

Ahmad Yahya Dawod, Mohammed Ali Sharafuddin
2021 Bulletin of Electrical Engineering and Informatics  
The performance of machine learning algorithms such as random forest (RF), support vector machine (SVM), decision tree (DT), and object-based nearest neighbors (NN) algorithms were used in this study to  ...  SVM with a radial basis function was used to classify the remainder of the images, resulting in an overall accuracy of 96.83%. Precision and recall reached 93.33 and 96%, respectively.  ...  for a sample issue𝑚, and 𝑠 is sample instances; 𝑇 𝑚 is the target value for a sample issue𝑚.  ... 
doi:10.11591/eei.v10i6.3199 fatcat:htxlilvcxreqbhdlaf6adkk6im

Physics-based detection of targets in SAR imagery using support vector machines

B. Krishnapuram, J. Sichina, L. Carin
2003 IEEE Sensors Journal  
In SAR image formation, the multi-aspect signatures are integrated to form a single image, thereby losing explicit aspect dependence.  ...  It is assumed that the SAR image has been passed through a prescreener [4] to identify the location of suspected targets, and in this paper such locations are referred to as a points of interest (POIs)  ...  ACKNOWLEDGEMENTS The authors would like to thank Gunnar Raetsch at GMD First, Germany, for graciously providing a preprint of a paper and for his discussion and comments on the SVM technique.  ... 
doi:10.1109/jsen.2002.805552 fatcat:luylxatrqzehjhahrp6js2amkq

An Improved GLRT Method for Target Detection in SAR Imagery

Yingyun Ju, Peng Fu, Xin Zhou, Rongkun Peng, D. Mingxing, X. Guosheng
2015 MATEC Web of Conferences  
A region-based generalized likelihood ratio test (GLRT) method is proposed in this paper, and this method combines the GLRT detection theory and image segmentation technology.  ...  Finally, with the knowledge of statistical characteristics of clutter and target, GLRT detection method is applied to the each pixel in the potential target region to obtain more accurate detection results  ...  GLRT target detection In the above section, the SAR image is roughly segmented to a land clutter region and potential target region.  ... 
doi:10.1051/matecconf/20153115004 fatcat:juk7edfxafbs3pbup3gt7dnnwq

Analysis of Covid-19 Trends: A Machine Learning Approach

Ammar Haider, Dr Mumtaz Ali Sheikh, Dr. Mumtaz Ali Sheikh
2022 Zenodo  
In this research, we focus on the problem of Covid-19 pandemic future forecasting trends prediction using machine learning and deep learning models.  ...  We used five machine learning and deep learning models in which VAR, Convo-1D, LSTM, SVR, DTR are included, on the Covid-19 dataset of twelve countries.  ...  Acknowledgements: I wish to acknowledge my deep appreciation to my supervisors of the Department of Computer Science, Forman Christian College (A Chartered University) who helped me finalize my thesis  ... 
doi:10.5281/zenodo.6508742 fatcat:6oa27g5cp5cj3orbumamemtgtu

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach [article]

Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassasni, Michal J. Wesolowski, Kevin A. Schneider, Ralph Deters
2020 arXiv   pre-print
While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to  ...  The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control.  ...  [16] proposed a machine learning approach for COVID-19 classification from CT images. Patches with different sizes 16×16, 32×32, 48×48, 64×64 were extracted from 150 CT images.  ... 
arXiv:2004.10641v1 fatcat:rc6uffdihnhbvf5wiy2eflt5w4

Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review

T. Aishwarya, V. Ravi Kumar
2021 SN Computer Science  
In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.  ...  Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease  ...  Customized Deep CNNs for Detection of Covid-19 Cases from Chest X-ray The study in [6] demonstrates a combined machine-human design approach utilizing a network called the COVID-Net.  ... 
doi:10.1007/s42979-021-00605-9 pmid:33899005 pmcid:PMC8056995 fatcat:g73t2z7jebgxzmxxjkwrsg7y54

Video SAR Moving Target Detection Using Dual Faster R-CNN

Liwu Wen, Jinshan Ding, Otmar Loffeld
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The classical shadow-aided detection was applied in video SAR, and most recently, the deep learning approach has been developed for shadow-aided moving target detection.  ...  Index Terms-Deep learning, ground moving target indication (GMTI), radar imaging, shadow detection, video synthetic aperture radar (SAR).  ...  This joint detection approach fully uses the target features in both the SAR image and RD spectrum, which enables a good detection performance and significant improvement in false alarm. IV.  ... 
doi:10.1109/jstars.2021.3062176 fatcat:rggehyemk5emjdx5yv6r2v2hem

A Machine Learning Approach as an Aid for Early COVID-19 Detection

Roberto Martinez-Velazquez, Diana P. Tobón V., Alejandro Sanchez, Abdulmotaleb El Saddik, Emil Petriu
2021 Sensors  
These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19.  ...  In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms.  ...  Acknowledgments: Especial acknowledgements to the National Council of Science and Technology (CONACyT) and the State Council of Science and Technology Colima (CECyTCOL), both in México.  ... 
doi:10.3390/s21124202 fatcat:qrt4gevnlndzndg6fm6jtyscly

Intelligent Electronic Navigational Aids: A New Approach

Costin Barbu, Maura Lohrenz, Geary Layne
2006 2006 5th International Conference on Machine Learning and Applications (ICMLA'06)  
In this paper we present two approaches to assist a user with target detection and clutter analysis, and we suggest how these tools could be integrated with an electronic chart system.  ...  The first tool, an information fusion technique, is a multiple-view generalization of AdaBoost, which can assist a user in finding a target partially obscured by display clutter.  ...  In addition, both local metrics (saliency and clutter) were significantly different than the global metrics for images with the HUD overlays, providing another cue for detecting this target.  ... 
doi:10.1109/icmla.2006.30 dblp:conf/icmla/BarbuLL06 fatcat:cz6gmwpm6fbqteiykipfihlv7a

Swarm Learning as a privacy-preserving machine learning approach for disease classification [article]

Stefanie Warnat-Herresthal, Hartmut Schultze, Krishna Prasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Haendler, Peter Pickkers, N Ahmad Aziz, Sofia Ktena, Christian Siever (+21 others)
2020 bioRxiv   pre-print
To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge  ...  We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes.  ...  540 technology and decentralized machine learning in an entirely democratized approach 541 .  ... 
doi:10.1101/2020.06.25.171009 fatcat:gv3qbxdjcjasrbo7ylszhdazja
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