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Classification of hyperspectral data from urban areas based on extended morphological profiles
Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed.Expand
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Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles
Classification of hyperspectral data with high spatial resolution from urban areas is discussed. An approach has been proposed which is based on using several principal components from theExpand
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Random Forests for land cover classification
Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have beenExpand
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Parallel consensual neural networks
A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data.Expand
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Automatic Spectral–Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction
A robust framework for the classification of hyperspectral images which takes into account both spectral and spatial information is proposed. The extended multivariate attribute profile (EMAP) isExpand
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Mapping of hyperspectral AVIRIS data using machine-learning algorithms
Hyperspectral imaging provides detailed spectral and spatial information from the land cover that enables a precise differentiation between various surface materials. On the other hand, theExpand
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Hyperspectral Feature Extraction Using Total Variation Component Analysis
In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted byExpand
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Multiple classifiers applied to multisource remote sensing data
The combination of multisource remote sensing and geographic data is believed to offer improved accuracies in land cover classification. For such classification, the conventional parametricExpand
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A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data
Classification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sourcesExpand
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Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis
Classification of high-resolution hyperspectral data is investigated. Previously, in classification of high-resolution panchromatic data, simple morphological profiles have been constructed with aExpand
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