Search results for "Hyperspectral image classification"

showing 3 items of 3 documents

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

2020

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…

010504 meteorology & atmospheric sciencesComputer sciencehyperspectral image classificationScience0211 other engineering and technologiesgeoinformatics02 engineering and technologyneuroverkot01 natural sciencesConvolutional neural networkpuulajitPARAMETERSSet (abstract data type)LIDARFORESTSClassifier (linguistics)021101 geological & geomatics engineering0105 earth and related environmental sciencesbusiness.industryDeep learningspektrikuvausQHyperspectral imagingdeep learningPattern recognition15. Life on landmiehittämättömät ilma-aluksetPerceptron113 Computer and information sciencesClass (biology)drone imagery3d convolutional neural networksmetsänarviointiMACHINEkoneoppiminentree species classification3D convolutional neural networksGeneral Earth and Planetary SciencesRGB color modelArtificial intelligencekaukokartoitusbusinesshyperspectral image classificationRemote Sensing
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A survey of active learning algorithms for supervised remote sensing image classification

2011

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active …

FOS: Computer and information sciencesComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionMachine learningcomputer.software_genreactive learningHyperspectral image classificationEntropy (information theory)Electrical and Electronic EngineeringArchitectureRemote sensingvery high resolution (VHR)PixelContextual image classificationbusiness.industryHyperspectral imagingSupport vector machinehyperspectraltraining set definitionSignal Processingsupport vector machine (SVM)Artificial intelligenceHeuristicsbusinessAlgorithmcomputerimage classification
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Hyperspectral Image Classification with Kernels

2007

The information contained in hyperspectral images allows the characterization, identification, and classification of land covers with improved accuracy and robustness. However, several critical problems should be considered in the classification of hyperspectral images, among which are (a) the high number of spectral channels, (b) the spatial variability of the spectral signature, (c) the high cost of true sample labeling, and (d) the quality of data. Recently, kernel methods have offered excellent results in this context. This chapter reviews the state-of-the-art hyperspectral image classifiers, presents two recently proposed kernel-based approaches, and systematically discusses the specif…

Kernel methodSpectral signaturebusiness.industryComputer scienceHyperspectral image classificationPattern recognitionSpatial variabilityArtificial intelligencebusiness
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