[PDF] Top 20 Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
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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 ...Ineffective ... See full document
14
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 ...Ineffective ... See full document
15
Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
... the hyperspectral images obtained may contain severe noise, where the corresponding band image is effectively useless as it has no correlation to any adjacent bands (see in ...the classification ... See full document
9
Joint bilateral filtering and spectral similarity-based sparse representation : a generic framework for effective feature extraction and data classification in hyperspectral imaging
... range, hyperspectral imaging (HSI) has been an important surveillance and reconnaissance technology for military [1] as well as other civil applications such as food quality assessment [2, 3], pharmaceutical ... See full document
22
Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis
... more effective feature ...spectral feature extraction techniques, the proposed method always stands out with the highest classification ...University, classification results can ... See full document
16
Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral-spatial classification of hyperspectral images
... for feature extraction, followed by ELM for ...for classification with feature extracted using local binary pattern (LBP) and Gabor ...Augmented Sparse Multinomial Logistic ELM ... See full document
15
Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images
... HSI classification is to assign each pixel of the hypercube into a different class according to the spectral and spatial characteristics ...image classification, such as the support vector machine ... See full document
15
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
... Graph-based learning [11] addresses the spatial relationship among pixels considering semi-supervised ...HSI images can lead to computational ...dictionary-based sparse representation, considering ... See full document
18
Feature extraction and classification for hyperspectral remote sensing images
... improved classification by utilizing both unlabeled and limited labeled data gained popularity in the machine learning ...semi-supervised learning methods include Co-Training [62] and ... See full document
162
Convolutional neural network extreme learning machine for effective classification of hyperspectral images
... solution, extreme learning machine (ELM) has attracted increasingly attentions in pattern recognition such as face recognition and hyperspectral image (HSI) ...HSI classification ... See full document
19
Spatial and Spectral Nonparametric Linear Feature Extraction Method for Hyperspectral Image Classification
... to classification tasks is ...the classification of HSI data in recent years ...nonparametric feature e xtraction method, integrating both spectral and spatial information, is ... See full document
5
A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine
... There are a variety of methods of how to divide the EEG dataset into training and testing datasets. To reduce the bias of training and testing data, a 10-fold cross-valida- tion technique is used. 10-fold ... See full document
12
An Extreme Learning Machine Approach to Effective Energy Disaggregation
... the multilayer architectures as a whole that is fine-tuned by several passes of back-propagation (BP) based fine-tuning in order to obtain reasonable learning capabilities – such a training scheme is ... See full document
18
Sparse Kernel feature extraction
... our feature extraction approaches, sparsity resulted in algorithms with simple implementations, and that scaled linearly in the number of examples in both computational and memory ...was effective in ... See full document
163
An Extreme Learning Machine for Biomedical Image classification: A Review
... ABSTRACT: Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights ... See full document
6
Unsupervised spectral sub-feature learning for hyperspectral image classification
... supervised learning approaches, semi-supervised methods for HSI classi fi cation make use of only limited labelled data together with unlabelled ...semi-supervised learning methods are a viable option for ... See full document
20
Multilayer Fuzzy Extreme Learning Machine Applied to Active classification and Transport of objects using an Unmanned Aerial Vehicle
... of Multilayer ELM and T1 FLS theory is suggested for active object classification and their ...The learning approach of the ML-FELM involves two main steps, first a number of Fuzzy Autoencoders are ... See full document
9
Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging
... for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy in pixel-based classification ...efficient feature extraction in ...data ... See full document
9
Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification
... In this section, the proposed NSLS-GSRC method is evaluated using three widely used hyperspectral data sets. The first one is the Indian Pines scene collected by the Airborne Visible/Infrared Imaging Spectrometer ... See full document
19
Application of Extreme Learning Machine in Fault Classification of Power Transformer
... art Extreme Learning Machine method offers a competitively good solution for complex ...The Extreme learning Machine is a recent second generation neural network ...other ... See full document
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