[PDF] Top 20 Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images
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Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images
... new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good ...to hyperspectral image (HSI) classification, the ... See full document
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Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral-spatial classification of hyperspectral images
... Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the ... See full document
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Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images
... the classification accuracy. Within the aforementioned methods, the spatial information of HSI is usually extracted from a fixed-size window or multiscale square windows, which also increases the ... See full document
17
Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields
... Conventional spectral classifiers treat hyperspectal images as a list of spectral measurements ...Vector Machine (SVM) classifiers have received significant attention lately because of their ... See full document
11
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 ... See full document
32
Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns
... in spectral-spatial features classification for hyperspectral images (HSI) with high spatial ...novel Spectral-spatial classification method for improving ... See full document
23
Spectral-Spatial Classification Integrating Band Selection for Hyperspectral Imagery With Severe Noise Bands
... Abstract—Spectral-spatial classification for hyperspectral im- agery has been receiving much attention, since the detailed spectral and rich spatial information of ... See full document
13
Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling
... analysis, classification and target detection, deep learning has also been introduced [27]-[30], because of its powerful capacity of unsupervised deep features self- ...HSI classification based on ... See full document
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Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling
... analysis, classification and target detection, deep learning has also been introduced [27]-[30], because of its powerful capacity of unsupervised deep features self- ...HSI classification based on ... See full document
12
Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns
... in spectral-spatial features classification for hyperspectral images (HSI) with high spatial ...novel Spectral-spatial classification method for improving ... See full document
22
Convolutional neural network extreme learning machine for effective classification of hyperspectral images
... With spectral information in hundreds of continuous narrow bands and spatial information acquired simultaneously, hyperspectral imaging has facilitated a number of applications, such as agricultural, ... See full document
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Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... Abstract—Although Extreme Learning Machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main ...of ... See full document
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Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... Abstract—Although Extreme Learning Machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main ...of ... See full document
14
Spectral Classification of a Set of Hyperspectral Images using the Convolutional Neural Network, in a Single Training
... take images of different sizes, each image Xi of size (Hi, Wi, Ni), will be converted towards the matrix format noted Mi, of size (number of lines Li = Hi x Wi and number of columns ... See full document
7
SPECTRAL-SPATIAL CLASSIFICATION OF SPECTRAL IMAGES WITH SUPER PIXEL-BASED DISCRIMINATIVE SPARES MODEL
... other classification approaches have focused on the design of effective feature extraction or reduction techniques, such as the principle component analysis, clonal selection feature-selection, kernel ... See full document
6
An Extreme Learning Machine for Biomedical Image classification: A Review
... Extreme Learning Machine (ELM) is swiftly gaining popularity as a way to train Single hidden Layer Feed- forward Networks (SLFN) for its attractive ...fast learning network with remarkable ... See full document
6
Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification
... feature learning, which has a quick access to arbitrary amounts of unlabeled data, is conceptually of high ...unsupervised spectral–spatial feature learning of hyperspectral ... See full document
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Real-time target detection in hyperspectral images based on spatial-spectral information extraction
... demonstrated that the correlation and covariance matri- ces can be calculated and updated in a causal manner, so the real-time processing requirement was met [10]. Du and Ren [11] presented a real-time constrained ... See full document
15
Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images
... Hyperspectral (HS) sensors collect information on a very high number of wavelengths, corresponding to tens or hundreds of bands. These kinds of data become increasingly popular and are extremely useful in several ... See full document
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On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification
... for hyperspectral images, the random sampling is usually undertaken on the same ...the spatial correlation between training and testing ...based spectral analysis methods in which no ... See full document
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