Cucumber Powdery Mildew Detection Using Hyperspectral Data

This study aimed to understand the spectral changes induced by Podosphaera xanthii, the causal agent of powdery mildew, in cucumber leaves from the moment of inoculation until visible symptoms are apparent. A Principal Component Analysis (PCA) was applied to the spectra to assess the spectral separability between healthy and infected leaves. A spectral ratio between infected and healthy leaf spectra was used to determine the best wavelengths for detecting the disease. Additionally, the spectra were used to compute two spectral variables, i.e., the Red-Well Point (RWP) and the Red-Edge Inflexion Point (REP). A linear Support Vector Machine (SVM) classifier was applied to certain spectral features to assess how well these features can separate the infected leaves from the healthy ones. The PCA showed that a good separability could be achieved from 4 DPI. The best model to fit the RWP and REP wavelengths corresponded to a linear model. The linear model had a higher adjusted R2 for the infected leaves than for the healthy leaves. The SVM trained with five first principal components scores achieved an overall accuracy of 95% at 4 DPI, i.e., two days before the visible symptoms. With the RWP and REP parameters, the SVM accuracy increased as a function of the day of inoculation, reaching 89% and 86%, respectively, when symptoms were visible at 6 DPI. Further research must consider a higher number of samples and more temporal repetitions of the experiment.

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Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

Design Engineering ◽

10.17762/de.vi.7730 ◽

2021 ◽

pp. 6787-6794

Author(s):

Anisha Rebinth, Dr. S. Mohan Kumar

Keyword(s):

Principal Component Analysis ◽

Gabor Filter ◽

Principal Component ◽

Component Analysis ◽

Disease Diagnosis ◽

Image Features ◽

Support Vector ◽

Svm Classifier ◽

Feature Descriptor ◽

Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

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Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090507 ◽

2020 ◽

Vol 9 (9) ◽

pp. 507

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Stuart Phinn

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Algorithms ◽

Principal Component ◽

Hyperspectral Data ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Forest Height ◽

Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

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Classifying Lithofacies from Textural Features in Whole Core CT-Scan Images

SPE Reservoir Evaluation & Engineering ◽

10.2118/205354-pa ◽

2021 ◽

pp. 1-17

Author(s):

Kurdistan Chawshin ◽

Carl F. Berg ◽

Damiano Varagnolo ◽

Andres Gonzalez ◽

Zoya Heidari ◽

...

Keyword(s):

Principal Component ◽

Ct Images ◽

Nondestructive Method ◽

Training Data ◽

Support Vector ◽

Svm Classifier ◽

Statistical Features ◽

Data Set ◽

Unseen Data ◽

Core Description

Summary X-ray computerized tomography (CT) is a nondestructive method of providing information about the internal composition and structure of whole core reservoir samples. In this study we propose a method to classify lithology. The novelty of this method is that it uses statistical and textural information extracted from whole core CT images in a supervised learning environment. In the proposed approaches, first-order statistical features and textural grey-levelco-occurrence matrix (GLCM) features are extracted from whole core CT images. Here, two workflows are considered. In the first workflow, the extracted features are used to train a support vector machine (SVM) to classify lithofacies. In the second workflow, a principal component analysis (PCA) step is added before training with two purposes: first, to eliminate collinearity among the features and second, to investigate the amount of information needed to differentiate the analyzed images. Before extracting the statistical features, the images are preprocessed and decomposed using Haar mother wavelet decomposition schemes to enhance the texture and to acquire a set of detail images that are then used to compute the statistical features. The training data set includes lithological information obtained from core description. The approach is validated using the trained SVM and hybrid (PCA + SVM) classifiers to predict lithofacies in a set of unseen data. The obtained results show that the SVM classifier can predict some of the lithofacies with high accuracy (up to 91% recall), but it misclassifies, to some extent, similar lithofacies with similar grain size, texture, and transport properties. The SVM classifier captures the heterogeneity in the whole core CT images more accurately compared with the core description, indicating that the CT images provide additional high-resolution information not observed by manual core description. Further, the obtained prediction results add information on the similarity of the lithofacies classes. The prediction results using the hybrid classifier are worse than the SVM classifier, indicating that low-power components may contain information that is required to differentiate among various lithofacies.

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Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Signal Processing ◽

10.1109/jstsp.2011.2123077 ◽

2011 ◽

Vol 5 (3) ◽

pp. 618-628 ◽

Cited By ~ 70

Author(s):

Wei Di ◽

Melba M. Crawford

Keyword(s):

Active Learning ◽

Image Classification ◽

Hyperspectral Image ◽

Computational Cost ◽

Hyperspectral Data ◽

Unlabeled Data ◽

Support Vector ◽

Svm Classifier ◽

Small Subset ◽

Hyperspectral Image Classification

A novel co-regularization framework for active learning is proposed for hyperspectral image classification. The first regularizer explores the intrinsic multi-view information embedded in the hyperspectral data. By adaptively and quantitatively measuring the disagreement level, it focuses only on samples with high uncertainty and builds a contention pool which is a small subset of the overall unlabeled data pool, thereby mitigating the computational cost. The second regularizer is based on the “consistency assumption” and designed on a spatial or the spectral based manifold space. It serves to further focus on the most informative samples within the contention pool by penalizing rapid changes in the classification function evaluated on proximally close samples in a local region. Such changes may be due to the lack of capability of the current learner to describe the unlabeled data. Incorporating manifold learning into the active learning process enforces the clustering assumption and avoids the degradation of the distance measure associated with the original high-dimensional spectral features. One spatial and two local spectral embedding methods are considered in this study, in conjunction with the support vector machine (SVM) classifier implemented with a radial basis function (RBF) kernel. Experiments show excellent performance on AVIRIS and Hyperion hyperspectral data as compared to random sampling and the state-of-the-art SVMSIMPLE.

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The recognition of plastic bottle using linear multi hierarchical SVM classifier

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202729 ◽

2021 ◽

pp. 1-14

Author(s):

LiHua Cai ◽

Jin Cao ◽

MingQiang Wang ◽

Ta Zhou ◽

HaiFeng Fang

Keyword(s):

Loss Function ◽

Deep Structure ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Svm Classifier ◽

Input Matrix ◽

Classification Rate ◽

Pet Bottles ◽

Combination Methods

Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.

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Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier

Plants ◽

10.3390/plants9080936 ◽

2020 ◽

Vol 9 (8) ◽

pp. 936 ◽

Cited By ~ 2

Author(s):

Jinling Zhao ◽

Yan Fang ◽

Guomin Chu ◽

Hao Yan ◽

Lei Hu ◽

...

Keyword(s):

Powdery Mildew ◽

Hyperspectral Imaging ◽

Blumeria Graminis ◽

Machine Learning Algorithms ◽

Plant Diseases ◽

Identification Accuracy ◽

Hyperspectral Images ◽

Support Vector ◽

Svm Classifier ◽

Measurement Technology

Powdery mildew (PM, Blumeria graminis f. sp. tritici) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.

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Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

Remote Sensing ◽

10.3390/rs11111338 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1338 ◽

Cited By ~ 12

Author(s):

Camile Sothe ◽

Michele Dalponte ◽

Cláudia Maria de Almeida ◽

Marcos Benedito Schimalski ◽

Carla Luciane Lima ◽

...

Keyword(s):

Tree Species ◽

Point Cloud ◽

Species Recognition ◽

Subtropical Forest ◽

Hyperspectral Data ◽

Support Vector ◽

Svm Classifier ◽

Kappa Index ◽

Species Classification ◽

Tree Species Classification

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset.

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Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance

PLoS ONE ◽

10.1371/journal.pone.0257008 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257008

Author(s):

Shuang Liu ◽

Haiye Yu ◽

Yuanyuan Sui ◽

Haigen Zhou ◽

Junhe Zhang ◽

...

Keyword(s):

Support Vector Machine ◽

Leaf Spot ◽

Principal Component ◽

Relevant Information ◽

Hyperspectral Data ◽

Support Vector ◽

Leaf Spot Disease ◽

Hyperspectral Reflectance ◽

Frogeye Leaf Spot ◽

Effective Principal

In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1–DS14) and used as inputs to build the classification models. Models’ performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).

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Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

10.21203/rs.3.rs-136988/v1 ◽

2021 ◽

Author(s):

SANTI BEHERA ◽

PRABIRA SETHY

Keyword(s):

Support Vector Machine ◽

Skin Cancer ◽

Principal Component ◽

Skin Lesions ◽

Classification Model ◽

Healthcare Sector ◽

Support Vector ◽

Svm Classifier ◽

Deep Feature ◽

Pigmented Skin Lesions

Abstract The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance; brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: first, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM. Then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12288. The highest results are obtained with 12288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 & VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

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Voice Feature Extraction for Gender and Emotion Recognition

ITM Web of Conferences ◽

10.1051/itmconf/20214003008 ◽

2021 ◽

Vol 40 ◽

pp. 03008

Author(s):

Madhu M. Nashipudimath ◽

Pooja Pillai ◽

Anupama Subramanian ◽

Vani Nair ◽

Sarah Khalife

Keyword(s):

Feature Extraction ◽

Data Extraction ◽

Principal Component ◽

Feature Reduction ◽

Healthcare Sector ◽

Support Vector ◽

Svm Classifier ◽

Human Machine Interaction ◽

Interaction Domain ◽

Voice Activity Detector

Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoustical and perceptual variations along with a variety of emotions which bring their own specific perceptions. In order to explore this area, feature extraction requires pre-processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre-processing using Voice Activity Detector(VAD), feature extraction using Mel-Frequency Cepstral Coefficient(MFCC), feature reduction by Principal Component Analysis(PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in healthcare sector, virtual assistants, security purposes and other fields related to Human Machine Interaction domain.

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