Representation learning- Hyperspectral images
From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images
... the representation learnt by D-KSVD and LC-KSVD, REs tends to show that D-KSVD successfully estimated a repre- sentation of the data (without being easily interpretable) whereas LC-KSVD seems to focus mostly on ...
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Hyperspectral and multispectral image fusion based on a sparse representation
... natural images. Learning a dictionary from the image of interest is commonly referred to as dictionary learning ...multiple images using a sparse regularization based on the decomposition of ...
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Frontiers in Spectral-Spatial Classification of Hyperspectral Images
... classifiers. To this end, computational properties are also recalled and examples of experimental results are discussed for all considered algorithms. Three benchmark data sets, which include both widely known long-used ...
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Matrix cofactorization for joint representation learning and supervised classification : application to hyperspectral image analysis
... and representation learning are two widely used classes of methods to analyze multivariate ...of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and ...
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Hyperspectral image representation through alpha-trees
... hierarchical representation of an image into partitions of regions with increasing ...grayscale images and has been successfully used in the field of remote ...this representation can be adapted to ...
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Fusion of multispectral and hyperspectral images based on sparse representation
... MS images within a constrained optimization framework, by incorporating sparse reg- ularization using dictionaries learned from the observed ...ter learning the dictionaries and the corresponding supports ...
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Fusion of multispectral and hyperspectral images based on sparse representation
... sensing images since they do not necessarily adapt to the nature of the observed ...consequence, learning the dictionary from the observed images instead of using prede•ned bases generally improves ...
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Unsupervised representation learning in interactive environments
... 2.1 Abstract Self-supervised methods, wherein an agent learns representations solely by observing the results of its actions, become crucial in environments which do not provide a dense reward signal or have labels. In ...
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Nonlinear unmixing of hyperspectral images: Models and algorithms
... machine learning, after nonlinear transformation, a nonlinear mixture of R components can often be represented as a linear mixture of Ru endmembers, with ...
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Concept Generalization in Visual Representation Learning
... fer learning experiments on a particular set of concepts, ...tune learning rate and weight decay hyper-parameters on a validation set randomly sampled from the training set of each concept do- main (for ...
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Nonlinear Data Representation for Visual Learning
... Unit´e de recherche INRIA Lorraine, Technopˆole de Nancy-Brabois, Campus scientifique, ` NANCY 615 rue du Jardin Botanique, BP 101, 54600 VILLERS LES Unit´e de recherche INRIA Rennes, Ir[r] ...
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Representation learning in unsupervised domain translation
... invariant representation learning ...of representation learning from the task on gen- ...Conditional representation GAN, this allowed us to transfer high-level semantics from a source ...
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Unmixing multitemporal hyperspectral images with variability: an online algorithm
... single hyperspectral image ...multi-temporal hyperspectral im- ages, which can preclude the use of batch estimation procedures due to limited memory resources, contrasting with the batch approach proposed ...
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Extended faint source detection in astronomical hyperspectral images
... When the target spectra are unknown, it is necessary to resort to unsupervised techniques. The most used as a benchmark in automatic HSI source detection is the Reed-Xiaoli (RX) algo- rithm [15] and improvements (e.g. ...
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Nonlinear unmixing of hyperspectral images: Models and algorithms
... This article has described development methods in nonlin- ear mixing for hyperspectral imaging. Several important chal- lenges remain. First of all, better integration of algorithmic approaches and physical models ...
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Edge-preserving nonnegative deconvolution of hyperspectral fluorescence microscopy images
... restored images, ...deconvolved images are significantly denoised and bacteria edges are restored for both ...piecewise-constant images (hence better ...
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Nonlinear spectral unmixing of hyperspectral images using Gaussian processes
... Abstract—This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reßectances result from a nonlinear function of the abundance ...
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Self-supervised representation learning from electroencephalography signals
... professionals. Learning useful representations automatically from unlabeled biosignals could therefore drastically reduce the cost and time required to process such ...
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Towards learning sentence representation with self-supervision
... Supervised Learning In supervised learning, the assumption is that both the input and the desired label or target value are provided for the training ...supervised learning problems are ...
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Unmixing multitemporal hyperspectral images with variability: an online algorithm
... ABSTRACT Hyperspectral unmixing consists in determining the reference spec- tral signatures composing a hyperspectral image and their relative abundance fractions in each ...spectral images precludes ...
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