scholarly journals Spatial-Spectral Density Peaks Based Discriminant Projection for Classification of Membranous Nephropathy Hyperspectral Pathological Image1

2020 ◽  
Author(s):  
Meng Lv ◽  
Wei Li ◽  
Ran Tao
Keyword(s):  
Spectral Density ◽  
Membranous Nephropathy ◽  
Hyperspectral Image ◽  
Ex Vivo ◽  
Support Vector ◽  
Clinical Value ◽  
Density Peaks ◽  
B Virus ◽  
Discriminant Projection

Microscopic hyperspectral imaging has become an emerging technique for various medical applications. However, high dimensionality of hyperspectral image (HSI) makes image processing and extraction of important diagnostic information challenging. In this paper, a novel dimensionality reduction method named spatial-spectral density peaks based discriminant projection (SSDP) is proposed by considering spatial-spectral density distribution characteristics of immune complexes. The proposed SSDP coupled with support vector machine classifier (SVM) yields high-precision automatic diagnosis of membranous nephropathy (MN). Detailed ex-vivo validation of the proposed method demonstrates the potential clinical value of the system in identifying hepatitis B virus-associated membranous nephropathy (HBV-MN) and primary membranous nephropathy (PMN).

scholarly journals Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Chao Dong ◽  
Lianfang Tian
Keyword(s):  
Parallel Computing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Hyperspectral Image ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Training Procedure ◽  
Data Set ◽  
Computing Technique

Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations. The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library. The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms. It shows that the parallel RVMs accelerate the training procedure obviously.

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.

SUPPORT VECTOR CLASSIFICATION OF LAND COVER AND BENTHIC HABITAT FROM HYPERSPECTRAL IMAGES

2008 ◽  
Vol 18 (02) ◽  
pp. 337-348 ◽  
Author(s):  
VIDYA MANIAN ◽  
MIGUEL VELEZ-REYES
Keyword(s):  
Land Cover ◽  
Hyperspectral Image ◽  
Feature Vector ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Benthic Habitat ◽  
Hyperspectral Image Classification ◽  
Habitat Classification

This paper presents a novel wavelet and support vector machine (SVM) based method for hyperspectral image classification. A 1-D wavelet transform is applied to the pixel spectra, followed by feature extraction and SVM classification. Contrary to the traditional method of using pixel spectra with SVM classifier, our approach not only reduces the dimension of the input pixel feature vector but also improves the classification accuracy. Texture energy features computed in the spectral dimension are mapped using polynomial kernels and used for training the SVM classifier. Results with AVIRIS and other hyperspectral images for land cover and benthic habitat classification are presented. The accuracy of the method with limited training sets and computational burden is assessed.

scholarly journals An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data

2021 ◽  
Vol 10 (4) ◽  
pp. 242
Author(s):  
Shiuan Wan ◽  
Mei Ling Yeh ◽  
Hong Lin Ma
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Intelligent System ◽  
Image Data ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Hyperspectral Image Data

Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the research has currently focused on the classification of pixel-based image data for analysis. The study was carried out to develop a multi-category crop hyperspectral image classification system to identify the major crops in the Chiayi Golden Corridor. The hyperspectral image data from CASI (Compact Airborne Spectrographic Imager) were used as the experimental data in this study. A two-stage classification was designed to display the performance of the image classification. More specifically, the study used a multi-class classification by support vector machine (SVM) + convolutional neural network (CNN) for image classification analysis. SVM is a supervised learning model that analyzes data used for classification. CNN is a class of deep neural networks that is applied to analyzing visual imagery. The image classification comparison was made among four crops (paddy rice, potatoes, cabbages, and peanuts), roads, and structures for classification. In the first stage, the support vector machine handled the hyperspectral image classification through pixel-based analysis. Then, the convolution neural network improved the classification of image details through various blocks (cells) of segmentation in the second stage. A series of discussion and analyses of the results are presented. The repair module was also designed to link the usage of CNN and SVM to remove the classification errors.

Fusion classification of hyperspectral image by composite kernels support vector machine

2011 ◽  
Vol 19 (4) ◽  
pp. 878-883 ◽  
Author(s):  
高恒振 GAO Heng-zhen ◽  
万建伟 WAN Jian-wei ◽  
粘永健 NIAN Yong-jian ◽  
王力宝 WANG Li-bao ◽  
徐湛 XU Zhan
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Image ◽  
Support Vector
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