scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 1-5
Author(s):  
D. Akbari ◽  
M. Moradizadeh ◽  
M. Akbari
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Vector Machines ◽  
Neural Network Algorithm ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING A HYBRID FRAMEWORK

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 541-544
Author(s):  
D. Akbari
Keyword(s):  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Neural Network Classification ◽  
Vector Machines ◽  
Hybrid Framework ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING A HYBRID FRAMEWORK

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 41-44
Author(s):  
D. Akbari ◽  
M. Moradizadeh
Keyword(s):  
Spatial Information ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Quebec City ◽  
Spectral Variability ◽  
Hierarchical Segmentation ◽  
Spectral Identification ◽  
Vector Machines

Abstract. Hyperspectral Images are worthwhile data for many processing algorithms (e.g. Dimensionality Reduction, Target Detection, Change Detection, Classification and Unmixing). Classification is a key issue in processing hyperspectral images. Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. There are many algorithms for classification. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Quebec City hyperspectral dataset, demonstrate that the proposed approach achieves approximately 9% and 5% better overall accuracy than the MLP and the original MHS algorithms respectively.

scholarly journals A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES

2015 ◽  
Vol XL-1-W5 ◽  
pp. 37-41 ◽  
Author(s):  
D. Akbari ◽  
A. R. Safari ◽  
S. Homayouni ◽  
S. Khazai
Keyword(s):  
Spatial Information ◽  
Selection Process ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Washington Dc ◽  
Spatial Classification ◽  
Marker Selection ◽  
Vector Machines ◽  
Segmentation Algorithms

An effective approach based on the Minimum Spanning Forest (MSF), grown from automatically selected markers using Support Vector Machines (SVM), has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM) and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.

scholarly journals Research on Sentiment Classification Algorithms on Online Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Ruixia Yan ◽  
Zhijie Xia ◽  
Yanxi Xie ◽  
Xiaoli Wang ◽  
Zukang Song
Keyword(s):  
Neural Network ◽  
Sentiment Classification ◽  
Online Review ◽  
Support Vector ◽  
Classification Algorithms ◽  
Evaluation Indexes ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Reinforcement Learning Models

The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.

scholarly journals Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data

2010 ◽  
Vol 48 (7) ◽  
pp. 2880-2889 ◽  
Author(s):  
Björn Waske ◽  
Sebastian van der Linden ◽  
Jón Atli Benediktsson ◽  
Andreas Rabe ◽  
Patrick Hostert
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Vector Machines

scholarly journals Deep Learning and Handcrafted Features for Virus Image Classification

2020 ◽  
Vol 6 (12) ◽  
pp. 143
Author(s):  
Loris Nanni ◽  
Eugenio De Luca ◽  
Marco Ludovico Facin ◽  
Gianluca Maguolo
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Support Vector ◽  
Transmission Electron ◽  
Vector Machines ◽  
Multiple Support ◽  
Multiple Support Vector Machines

In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

scholarly journals CLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING

10.6036/10117 ◽  
2022 ◽  
Vol 97 (1) ◽  
pp. 35-38
Author(s):  
EDUARDO PEREZ CARETA ◽  
RAFAEL GUZMÁN SEPÚLVEDA ◽  
JOSE MERCED LOZANO GARCIA ◽  
MIGUEL TORRES CISNEROS ◽  
RAFAEL GUZMAN CABRERA
Keyword(s):  
Neural Network ◽  
Medical Diagnosis ◽  
Cross Validation ◽  
Support Vector ◽  
Computational Tools ◽  
Standard Methods ◽  
Vector Machines ◽  
Test Sets ◽  
Classification Of Images

The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.

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