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Powdery Mildew Caused by Erysiphe cruciferarum on Wild Rocket (Diplotaxis tenuifolia): Hyperspectral Imaging and Machine Learning Modeling for Non-Destructive Disease Detection

Catello Pane, Gelsomina Manganiello, Nicola Nicastro, Teodoro Cardi, Francesco Carotenuto
2021 Agriculture  
Shifts in the irradiance absorption by chlorophyll a of diseased leaves in the spectrum blue range seems to be at the base of the hyperspectral imaging detection of wild rocket powdery mildew.  ...  Hyperspectral image analysis allowed to characterize the spectral response of wild rocket affected by powdery mildew and the adopted machine-learning approach (a trained Random Forest model with the four  ...  The aim of research was to assess the first reported occurrence of powdery mildew on wild rocket by using high-resolution hyperspectral imaging and machine learning-based classification methods to discriminate  ... 
doi:10.3390/agriculture11040337 fatcat:f5liwovhyrge5gijjltqfbijy4

Investigation of the random forest framework for classification of hyperspectral data

J. Ham, Yangchi Chen, M.M. Crawford, J. Ghosh
2005 IEEE Transactions on Geoscience and Remote Sensing  
For both methods, classification results obtained from experiments on data acquired by the National Aeronautics and Space Administration (NASA) Airborne Visible/Infrared Imaging Spectrometer instrument  ...  Results are compared to a random forest implementation based on the framework of classification and regression trees.  ...  ACKNOWLEDGMENT The authors thank A. Neuenschwander (UT Center for Space Research) for help in preprocessing the Hyperion data and interpreting the overall classification results.  ... 
doi:10.1109/tgrs.2004.842481 fatcat:t6gxpls2srfxfmmhzm5yuv7lda

Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis

R Lawrence
2004 Remote Sensing of Environment  
We compared traditional CTA results to SGB for three remote sensing based data sets, an IKONOS image from the Sierra Nevada Mountains of California, a Probe-1 hyperspectral image from the Virginia City  ...  Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of  ...  Two major types of methods have been developed, those that develop new classification trees based on the results of previous classification trees (boosting methods) and those that rely on subsets of the  ... 
doi:10.1016/j.rse.2004.01.007 fatcat:qa2rwatc6zerzehm2fdvv5walu

Hyperspectral Image Classification [chapter]

Rajesh Gogineni, Ashvini Chaturvedi
2019 Processing and Analysis of Hyperspectral Data [Working Title]  
Further, the classification methods based on machine learning and the future directions are discussed.  ...  This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel-based methods, supervised and unsupervised classifiers, classification based on sparse representation  ...  Based on the usage of training sample, image classification task is categorized as supervised, unsupervised and semi-supervised hyperspectral image classification.  ... 
doi:10.5772/intechopen.88925 fatcat:7ixv44bobbd3vkp7hn5c6tlb2y

An Active Learning Approach to Hyperspectral Data Classification

S. Rajan, J. Ghosh, M.M. Crawford
2008 IEEE Transactions on Geoscience and Remote Sensing  
Our interleaved semi-18 supervised active learning method was tested on both single and 19 spatially/temporally related hyperspectral data sets.  ...  Our interleaved semi-18 supervised active learning method was tested on both single and 19 spatially/temporally related hyperspectral data sets.  ...  ACKNOWLEDGMENT 718 The authors would like to thank A. Neunschwander and 719 Y. Chen for help in preprocessing the Hyperion data. 720 ACKNOWLEDGMENT 718 The authors would like to thank A.  ... 
doi:10.1109/tgrs.2007.910220 fatcat:7h75s4gs6zh23d7wqwptle6cla

Support vector machines in remote sensing: A review

Giorgos Mountrakis, Jungho Im, Caesar Ogole
2011 ISPRS journal of photogrammetry and remote sensing (Print)  
A wide range of methods for analysis of airborne-and satellite-derived imagery continues to be proposed and assessed.  ...  A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010).  ...  Acknowledgements Support was provided by the National Science Foundation (award GRS-0648393), by the National Aeronautics and Space Administration (awards NNX08AR11G, NNX09AK16G) and by the Syracuse Center  ... 
doi:10.1016/j.isprsjprs.2010.11.001 fatcat:6hx57jxaxvfxvjoqqmhk5puhty

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Mengmeng Sun, Chunyang Wang, Shuangting Wang, Zongze Zhao, Xiao Li
2018 Advances in Multimedia  
The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish  ...  The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification  ...  In the steps of the semisupervised random forest hyperspectral remote sensing image classification method based on weighted entropy, the weighted entropy algorithm based on voting probability is used to  ... 
doi:10.1155/2018/3521720 fatcat:imtl5u5uljegxaqsys2dptqnbm

Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms

Timothy Dube, Onisimo Mutanga, Adam Elhadi, Riyad Ismail
2014 Sensors  
The results showed that the SGB algorithm yielded the best performance for intra-and-inter species biomass prediction; using all the predictor variables as well as based on the most important selected  ...  We demonstrated that OPEN ACCESS Sensors 2014, 14 15349 although the two statistical methods were able to predict biomass accurately, RF produced weaker results as compared to SGB when applied to combined  ...  Acknowledgments The authors are thankful to BlackBridge for accepting our proposal and providing the high spatial resolution RapidEye image of the study area free of charge.  ... 
doi:10.3390/s140815348 pmid:25140631 pmcid:PMC4179085 fatcat:ej7e5qjuhfhp5hg4xvxig5axzi

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

Emma Izquierdo-Verdiguier, Raúl Zurita-Milla
2020 International Journal of Applied Earth Observation and Geoinformation  
Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest.  ...  Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features.  ...  for pre-processing the WorldView-2 images.  ... 
doi:10.1016/j.jag.2020.102051 fatcat:cshllfratvfrfdyep47y2o3whm

Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles

Martina Pastorino, Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia
2021 Remote Sensing  
The proposed approach is also compared to previous methods that are based on alternate strategies for multimodal fusion.  ...  This is mostly advantageous for classification methods linked to multiresolution tasks formulated on hierarchical Markov models.  ...  Acknowledgments: The authors wish to thank CNES for partial financial funding, DigitalGlobe Inc. for the QuickBird data, Google Crisis Response for the GeoEye-1 data, and the Italian Space Agency (ASI)  ... 
doi:10.3390/rs13050849 fatcat:bcvpapkvrjcw3exwtkgvlndshi

Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

Yasenjiang Kahaer, Nigara Tashpolat, Qingdong Shi, Suhong Liu
2020 Water  
The soil hyperspectral data were processed by fractional order differential preprocessing method and optimized spectral indices method, and the Pearson correlation coefficient (PCC/r) analysis was made  ...  lack of shortwave infrared spectra, which made it possible to quantitatively retrieve saline SMC on the basis of Zhuhai-1 hyperspectral imagery.  ...  Finally, the authors wish to thank the referees for providing helpful suggestions for the improvement of the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/w12123360 fatcat:qh5y7q32dbblvg7f54256d2duu

Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

Zhanghua Xu, Qi Zhang, Songyang Xiang, Yifan Li, Xuying Huang, Yiwei Zhang, Xin Zhou, Zenglu Li, Xiong Yao, Qiaosi Li, Xiaoyu Guo
2022 Forests  
Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient  ...  boosting tree (XGBoost) machine learning algorithms.  ...  A special thanks to Zhaoquan Zhong, Xianyun Lin, Huafeng Zhang and Juyuan Gao for their help in the field experiment of this research. Comments made by the anonymous reviewers are greatly appreciated.  ... 
doi:10.3390/f13030418 fatcat:ptxa3cnwvrh4jgi6tq6f34mbjy

A survey of image classification methods and techniques for improving classification performance

D. Lu, Q. Weng
2007 International Journal of Remote Sensing  
This section focuses on the description of the major steps that may be involved in image classification. 2.  ...  Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification.  ...  Improving classification performance 849 Downloaded by [207.241.229.243] at 11:53 05 November 2017  ... 
doi:10.1080/01431160600746456 fatcat:xs7y7x4bpfhnfn5ahesllpchei

PREDICTING THE INFRARED UAV IMAGERY OVER THE COAST

A. Collin, D. James, A. Mury, M. Letard, B. Guillot
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models.  ...  This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints.  ...  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2021 XXIV ISPRS Congress (2021 edition)  ... 
doi:10.5194/isprs-archives-xliii-b1-2021-149-2021 fatcat:ve6eqmpj6rdgvh35sshwxi2pwq

Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques

Samuel N. Araya, Anna Fryjoff-Hung, Andreas Anderson, Joshua H. Viers, Teamrat A. Ghezzehei
2021 Hydrology and Earth System Sciences  
The boosted regression tree algorithm was marginally the best, with a mean absolute error of 3.8 % volumetric moisture content.  ...  Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing and machine learning.  ...  This paper was edited by Giuliano Di Baldassarre and reviewed by Salvatore Manfreda and one anonymous referee.  ... 
doi:10.5194/hess-25-2739-2021 fatcat:aweqrgv765glfiltmb524q2n6u
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