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Quantum Annealer for Subset Feature Selection and the Classification of Hyperspectral Images

Soronzonbold Otgonbaatar, Mihai Datcu
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Thus, we adapted our MI-based optimization problem for selecting highly-informative bands for each class of a given hyperspectral image to be run on a D-Wave quantum annealer.  ...  Hyperspectral images showing objects belonging to several distinct target classes are characterized by dozens of spectral bands being available.  ...  for Quantum computing (JUNIQ) on a D-Wave quantum annealer.  ... 
doi:10.1109/jstars.2021.3095377 fatcat:ol2bfrvqhvft5lr2ancj7exc74

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms  ...  different number of clusters [68] , and linear and quadratic decision stumps trained on randomly selected features [162] .  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Comparison of the Novel Probabilistic Self-Optimizing Vectorized Earth Observation Retrieval Classifier with Common Machine Learning Algorithms

Jan Pawel Musial, Jedrzej Stanislaw Bojanowski
2022 Remote Sensing  
retrieval based on a pre-computed look-up vector (LUV); and (6) separate parametrization of the algorithm for each discrete feature class (e.g., land cover).  ...  On the contrary, VEOR did not feature good classification skills for significantly distorted or for small datasets.  ...  In practice, initially, AdaBoost assigns equal weights to all training samples and selects the first decision stump with the highest classification skill expressed by a logistic loss function.  ... 
doi:10.3390/rs14020378 fatcat:tidiezrfhrc25nnttakqz2dk3a

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

Daniel Queirós da da Silva, Filipe Neves dos dos Santos, Armando Jorge Sousa, Vítor Filipe, José Boaventura-Cunha
2021 Computation  
This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research  ...  Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.  ...  with an AdaBoost classifier.  ... 
doi:10.3390/computation9120127 fatcat:m6e75lzcn5dpbh54xbszzgoiri

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

Shahab Eddin Jozdani, Brian Alan Johnson, Dongmei Chen
2019 Remote Sensing  
We tested the classifiers on two RS images (with spatial resolutions of 30 cm and 50 cm).  ...  ., size/shape/texture) for classifying urban LULC features, Geographic Object-Based Image Analysis (GEOBIA) techniques are commonly employed for mapping urban areas.  ...  GEOBIA deals with the shortcomings of pixel-based approaches by including an additional step in the classification phase, known as image segmentation.  ... 
doi:10.3390/rs11141713 fatcat:xfj3x76ycrgdff2jquzgumw3ny

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Abhinav Sharma, Arpit Jain, Prateek Gupta, Vinay Chowdary
2020 IEEE Access  
ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment.  ...  This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors  ...  Author's explored four classification models decision trees, stump decision trees, parallel decision trees, and random forest to discover SCC independent of Somatic Cell Count (SCC) which is widely used  ... 
doi:10.1109/access.2020.3048415 fatcat:7r4bjin7ang7bo3j4brv6kdxiu

CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020

2020 International Journal of Computer Assisted Radiology and Surgery  
means of verbal and written statements made by responsible authors, scrutinized by informed reviewers and utilized by an open-minded audience, with the aim to stimulate complimentary thoughts and actions  ...  With an increasing general demand and pressure on CARS to also go fully digital in the long term, many members of the CARS Congress Organizing Committee, however, are more cautious and convinced that one  ...  The work is supported by a grant-in-aid for scientific research on innovative areas, JSPS KAKENHI 17K17680. We thank Coordination for the Improvement of Higher Education Personnel (CAPES).  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

A learning framework for higher-order consistency models in multi-class pixel labeling problems [article]

Kyoungup Park, University, The Australian National, University, The Australian National
2015
One successful higher-order MRF model for scene understanding is the consistency model [Kohli and Kumar, 2010; Kohli et al., 2009] and earlier work by Ladicky et al. [2009, 2013] which contain higher-order  ...  potentials composed of lower linear envelope functions.  ...  Like other boosting classifiers, TextonBoost builds the sum of weak classifiers sharing features within a pool of decision stumps.  ... 
doi:10.25911/5d723cf464dee fatcat:3oxnwxfyerhcxgslerhjb4vh3m

Optimising the Impact of NRENs on Africa's Research

Margareth Gfrerer
2020
Optimising the Impact of NRENs on Africa's Research.  ...  Given the results suggesting that such differences may in fact play an important role in defining the position and the mix of services provided by KTTOs, further research is warranted to further investigate  ...  S VM It is a machine learning algorithm that is used for classification and regression. It is based on the idea of a decision plane that separates members belonging to different classes.  ... 
doi:10.20372/nadre/4657 fatcat:ihtmd2xltbhlne4cwkrcrts5pa