scholarly journals CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 411-416
Author(s):  
M. Gu ◽  
S. Lyu ◽  
M. Hou ◽  
S. Ma ◽  
Z. Gao ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Hyperspectral Image ◽  
Research Result ◽  
Principal Component ◽  
Shanxi Province ◽  
Support Vector ◽  
Archaeological Research ◽  
Naked Eye ◽  
Archaeological Work

There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

scholarly journals Multi-Scale Superpixels Dimension Reduction Hyperspectral Image Classification Algorithm Based on Low Rank Sparse Representation Joint Hierarchical Recursive Filtering

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3846
Author(s):  
Shenming Qu ◽  
Xuan Liu ◽  
Shengbin Liang
Keyword(s):  
Sparse Representation ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Decision Fusion ◽  
Block Matching ◽  
Low Rank ◽  
Support Vector ◽  
Recursive Filtering ◽  
Multi Scale

The original Hyperspectral image (HSI) has different degrees of Hughes phenomenon and mixed noise, leading to the decline of classification accuracy. To make full use of the spatial-spectral joint information of HSI and improve the classification accuracy, a novel dual feature extraction framework joint transform domain-spatial domain filtering based on multi-scale-superpixel-dimensionality reduction (LRS-HRFMSuperPCA) is proposed. Our framework uses the low-rank structure and sparse representation of HSI to repair the unobserved part of the original HSI caused by noise and then denoises it through a block-matching 3D algorithm. Next, the dimension of the reconstructed HSI is reduced by principal component analysis (PCA), and the dimensions of the reduced images are segmented by multi-scale entropy rate superpixels. All the principal component images with superpixels are projected into the reconstructed HSI in parallel. Secondly, PCA is once again used to reduce the dimension of all HSIs with superpixels in scale with hyperpixels. Moreover, hierarchical domain transform recursive filtering is utilized to obtain the feature images; ultimately, the decision fusion strategy based on a support vector machine (SVM) is used for classification. According to the Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient on the three datasets (Indian Pines, University of Pavia and Salinas), the experimental results have shown that our proposed method outperforms other state-of-the-art methods. The conclusion is that LRS-HRFMSuperPCA can denoise and reconstruct the original HSI and then extract the space-spectrum joint information fully.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

scholarly journals Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis

2019 ◽  
Vol 11 (10) ◽  
pp. 1219 ◽  
Author(s):  
Lan Zhang ◽  
Hongjun Su ◽  
Jingwei Shen
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Principal Components ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Homogeneous Region ◽  
Kernel Principal Component Analysis ◽  
Nonlinear Features

Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%–0.74%, 3.88%–4.37%, and 0.39%–4.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%–2.68%, 0.12%–1.10%, and 0.01%–0.08%, respectively, when compared with the method based only on the most suitable fundamental image.

Image Texture Based Hybrid Diagnostic Tool for Kidney Disease Classification

2018 ◽  
Vol 8 (9) ◽  
pp. 1899-1908
Author(s):  
P. Sreelatha ◽  
M. Ezhilarasi
Keyword(s):  
Diagnostic Tool ◽  
Classification Accuracy ◽  
Wavelet Coefficient ◽  
Region Of Interest ◽  
Principal Component ◽  
Disease Classification ◽  
Image Texture ◽  
Support Vector ◽  
Adaptive Wavelet ◽  
Medical Diagnostic

The identification of chronic medical conditions and its associated mortality has led to the emergence of less invasive methods for medical diagnostic imaging. This work proposes a Computer Aided Diagnostic tool useful in automatic classification of kidney images as normal, simple cysts, kidney stones and the less investigated complex cystic renal cell carcinoma. The first part of the work investigates an effective despeckling algorithm with a proposed adaptive wavelet based denoising technique. Encouraging increased PSNR values ranging from 15 dB to 24 dB were obtained. Second part of work suggests a set of wavelet coefficient based feature set which showed a classification accuracy of 92.2%, better by 20.3% to 0.8% against existing methods. The final part of the work to develop a complete tool for kidney image classification combines the proposed wavelet based features with three existing statistical based feature sets yielded a classification accuracy of 96.9%. The suggested features were extracted from the region of interest from an image set. A reduced feature set of 18 from the original size of 163 was obtained using principal component analysis and applied for training a support vector machine classifier.

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

2021 ◽  
Vol 16 (1) ◽  
pp. 56-74
Author(s):  
Weiwei Yang ◽  
Haifeng Song
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Support Vector ◽  
Svm Classifier ◽  
Spatial Classification ◽  
Homogeneous Regions

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

scholarly journals Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4391 ◽  
Author(s):  
Aimin Miao ◽  
Jiajun Zhuang ◽  
Yu Tang ◽  
Yong He ◽  
Xuan Chu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Procrustes Analysis ◽  
Waxy Maize ◽  
Maize Seeds ◽  
Variety Classification ◽  
Low Dimensional

Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.

Kinematic Gait Patterns in Competitive and Recreational Runners

2017 ◽  
Vol 33 (4) ◽  
pp. 268-276 ◽  
Author(s):  
Christian A. Clermont ◽  
Sean T. Osis ◽  
Angkoon Phinyomark ◽  
Reed Ferber
Keyword(s):  
Classification Accuracy ◽  
Three Dimensional ◽  
Principal Component ◽  
Treadmill Running ◽  
Support Vector ◽  
Gait Kinematics ◽  
Ankle Eversion ◽  
Machine Learning Approach ◽  
Recreational Runners ◽  
Time Point

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using multivariate analyses and a machine learning approach. Participants were allocated to the ‘competitive’ (n = 20) or ‘recreational’ group (n = 15) based on age, sex, and recent race performance. Three-dimensional (3D) kinematic data were collected during treadmill running at 2.7 m/s. A support vector machine (SVM) was used to determine if the groups were separable and classifiable based on kinematic time point variables as well as principal component (PC) scores. A cross-fold classification accuracy of 80% was found between groups using the top 5 ranked time point variables, and the groups could be separated with 100% cross-fold classification accuracy using the top 14 ranked PCs explaining 60.29% of the variance in the data. The features were primarily related to pelvic tilt, as well as knee flexion and ankle eversion in late stance. These results suggest that competitive and recreational runners have distinct, ‘typical’ running patterns that may help explain differences in injury mechanisms.

scholarly journals Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5154 ◽  
Author(s):  
Bo Liu ◽  
Ru Li ◽  
Haidong Li ◽  
Guangyong You ◽  
Shouguang Yan ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Wavelet Coefficients ◽  
Weed Detection ◽  
Imaging Spectrometer ◽  
Spectral Bands ◽  
Spectrometer System ◽  
Field Imaging

Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

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