scholarly journals Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (24) ◽  
pp. 2906 ◽  
Author(s):  
Razika Bazine ◽  
Huayi Wu ◽  
Kamel Boukhechba
Keyword(s):  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Wiener Filter ◽  
Spatial Filtering ◽  
Training Sample ◽  
Spatial Filter ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Two Dimensional

In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.

scholarly journals Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1405 ◽  
Author(s):  
Razika Bazine ◽  
Huayi Wu ◽  
Kamel Boukhechba
Keyword(s):  
Hyperspectral Imagery ◽  
Wiener Filter ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Computational Time ◽  
Support Vector ◽  
Two Dimensional ◽  
Significant Information ◽  
Dct Domain ◽  
Frequency Components

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

scholarly journals An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+

2021 ◽  
Vol 11 (12) ◽  
pp. 5703
Author(s):  
Yifan Si ◽  
Dawei Gong ◽  
Yang Guo ◽  
Xinhua Zhu ◽  
Qiangsheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Learning Algorithms ◽  
Principal Component ◽  
Semantic Segmentation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Framework

DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.

VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

2019 ◽  
Vol 19 (03) ◽  
pp. 1950008
Author(s):  
MONALISA MOHANTY ◽  
PRADYUT BISWAL ◽  
SUKANTA SABUT
Keyword(s):  
Ventricular Tachycardia ◽  
Decision Tree ◽  
Ventricular Arrhythmias ◽  
Ventricular Tachyarrhythmia ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Tree Classifier ◽  
Frequency Features

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

scholarly journals Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 33 ◽  
Author(s):  
Firgan Feradov ◽  
Iosif Mporas ◽  
Todor Ganchev
Keyword(s):  
Visual Stimuli ◽  
Decomposition Methods ◽  
Automated Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Eeg Activity ◽  
Rbf Kernel ◽  
Eeg Features

There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications.

scholarly journals A Nonfiducial PPG-Based Subject Authentication Approach Using the Statistical Features of DWT-Based Filtered Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Turky N. Alotaiby ◽  
Fatima Aljabarti ◽  
Gaseb Alotibi ◽  
Saleh A. Alshebeili
Keyword(s):  
Iris Recognition ◽  
Feature Vector ◽  
Identity Authentication ◽  
Smart Devices ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Statistical Features ◽  
Authentication System ◽  
Signal Band

Nowadays, there is a global change in lifestyle that is moving more toward the use of e-services and smart devices which necessitate the verification of user identity. Different organizations have put into place a range of technologies, hardware, and/or software to authenticate users using fingerprints, iris recognition, and so forth. However, cost and reliability are significant limitations to the use of such technologies. This study presents a nonfiducial PPG-based subject authentication system. In particular, the photoplethysmogram (PPG) signal is first filtered into four signals using the discrete wavelet transform (DWT) and then segmented into frames. Ten simple statistical features are extracted from the frame of each signal band to compose the feature vector. Augmenting the feature vector with the same features extracted from the 1st derivative of the corresponding signal is investigated, along with different fusion approaches. A support vector machine (SVM) classifier is then employed for the purpose of identity authentication. The proposed authentication system achieved an average authentication accuracy of 99.3% using a 15 sec frame length with the augmented multiband approach.

scholarly journals Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Mi ◽  
Xin He ◽  
Haiwei Liu ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Training Sample ◽  
Detection Algorithm ◽  
Human Detection ◽  
Security Requirements ◽  
Support Vector ◽  
Svm Classifier ◽  
Histograms Of Oriented Gradients ◽  
Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

scholarly journals DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 651-656
Author(s):  
F. Samadzadega ◽  
H. Hasani
Keyword(s):  
Urban Area ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Classification Performance ◽  
Feature Subset Selection ◽  
Bees Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Feature Subset

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

scholarly journals GLCM – SVM based Classification of Brain MRI with K-Means Clusters

2020 ◽  
Vol 9 (3) ◽  
pp. 100-104
Keyword(s):  
Brain Mri ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Selection Approach ◽  
Random Manner ◽  
Feature Selection Approach ◽  
Occurrence Matrix ◽  
Mr Brain

In this proposed method, MR Brain image segmentation technique based on K-means clustering combined with Discrete Wavelet Transform (DWT) based feature extraction and Gray Level Co-Occurrence Matrix (GLCM) based feature selection approach has been presented. A Perfect Radial Basis Function (RBF) - Support Vector Machine (SVM) Classifier has been selected for this process. The Performance of the classifier was estimated through accuracy based on the fractions selectivity and sensitivity. Accuracy of the proposed classifier was found to be 93%. Moreover, in this proposed method, instead of selecting the cluster centres in a random manner, Histogram technique was used.

scholarly journals Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

2018 ◽  
Vol 7 (4.10) ◽  
pp. 935
Author(s):  
Vasudha Harlalka ◽  
Viraj Pradip Puntambekar ◽  
Kalugotla Raviteja ◽  
P. Mahalakshmi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Shannon Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Energy Entropy ◽  
Electroencephalogram Eeg ◽  
Logarithmic Energy

Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine     (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.  

A NEW CAD SYSTEM FOR BREAST CANCER CLASSIFICATION USING DISCRIMINATION POWER ANALYSIS OF WAVELET’S COEFFICIENTS AND SUPPORT VECTOR MACHINE

2020 ◽  
Vol 20 (06) ◽  
pp. 2050036
Author(s):  
NASSER EDINNE BENHASSINE ◽  
ABDELNOUR BOUKAACHE ◽  
DJALIL BOUDJEHEM
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Power Analysis ◽  
Region Growing ◽  
Pectoral Muscle ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Discrimination Power ◽  
Cad System

The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provide a second opinion for the early detection of breast cancer and therefore, aids to reduce the mortality rates. In this work, we try to develop a new (CAD) system to classify mammograms into benign or malignant. The proposed system consists of three main steps. The preprocessing stage consists of noise filtering, elimination of unwanted objects and suppressing the pectoral muscle. The Seeded Region Growing (SRG) segmentation technique is applied in a triangular region that contains the pectoral muscle to localize it and extract the region of interest (ROI). The features extraction step is performed by applying the discrete wavelet transform (DWT) to each obtained ROI, and the most discriminating coefficients are selected using the discrimination power analysis (DPA) method. Finally, the classification is carried out by the support vector machine (SVM), artificial neural networks (ANN), random forest (RF) and Naive Bayes (NB) classifiers. The evaluation of the proposed system on the mini-MIAS database shows its effectiveness compared to other recently published CAD systems, and a classification accuracy of about 99.41% with the SVM classifier was obtained.

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