Segmented Linear Discriminant Analysis for Hyperspectral Image Classification

In hyperspectral image (HSI) classification, feature extraction is one important step. Traditional methods, e.g., principal component analysis (PCA) and locality preserving projection, usually neglect the information of within-class similarity and between-class dissimilarity, which is helpful to the improvement of classification. On the other hand, most of these methods, e.g., PCA and linear discriminative analysis, consider that the HSI data lie on a low-dimensional manifold or each class is on a submanifold. However, some class data of HSI may lie on a multimanifold. To avoid these problems, we propose a method for feature extraction in HSIs, assuming that a local region resides on a submainfold. In our method, we deal with the data region by region by taking into account the different discriminative locality information. Then, under the metric learning framework, a robust distance metric is learned. It aims to learn a subspace in which the samples in the same class are as near as possible while the samples in different classes are as far as possible. Encouraging experimental results on two available hyperspectral data sets indicate that our proposed algorithm outperforms many existing feature extract methods for HSI classification.

Hyperspectral images are used to recognize and determine the objects on the earth’s surface. This image contains more number of spectral bands and classifying the image becoming a difficult task. Problems of higher number of spectral dimensions are addressed through feature extraction and reduction. However, accuracy and computational time are the important challenges involved in the classification of hyperspectral images. Hence in this paper, a supervised method has been developed to classify the hyperspectral image using support vector machine (SVM) and linear discriminant analysis (LDA). In this work, spectral features of the images are extracted and reduced using LDA. Spectral features of hyperspectral images are classified using SVM with RBF kernel like buildings, vegetation fields, etc. The simulation results show that the SVM algorithm combined with LDA has good accuracy and less computational time. Furthermore, the accuracy of classification is enhanced by incorporating the spatial features using edge-preserving filters.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to detect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.

Credit risk analysis forms an important criterion for the endurance of the banking sector. Understanding credit liability of customer enhances the security stability of the banker. This research is aimed at comparing the predictive accuracy of data mining techniques with feature selection and extraction techniques in the default of credit card clients and suggesting the most efficient technique for analyzing credit card default. Customer's default payments in Taiwan were used as the dataset. Machine Learning classification algorithms like K-nearest neighbor(KNN), Naive Bayesian(NB), Decision Tree(DT) and Support Vector Machines(SVM) were applied to the dataset. Feature filter methods like information gain (IG), gain ratio (GR) and chi-square (CS) and Wrapper based methods like Best First search(BFS) and Hill Climbing search (HCS) were used to identify minimum features required. Feature extraction methods such as Principal Component Analysis(PCA) and Linear Discriminant Anal-ysis(LDA) were also applied. The minimum features of feature selection algorithms along with PCA and LDA were then applied to Base classifiers and their accuracies were tabulated and compared. SVM with feature selection and extraction techniques had the best predictive accuracy.

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

Feature extraction is of high importance for effective data classification in hyperspectral imaging (HSI). Considering the high correlation among band images, spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (2D-SSA), which is a recent technique for generic data mining and temporal signal analysis, is proposed. With 2D-SSA applied to HSI, each band image is decomposed into varying trends, oscillations, and noise. Using the trend and the selected oscillations as features, the reconstructed signal, with noise highly suppressed, becomes more robust and effective for data classification. Three publicly available data sets for HSI remote sensing data classification are used in our experiments. Comprehensive results using a support vector machine classifier have quantitatively evaluated the efficacy of the proposed approach. Benchmarked with several state-of-the-art methods including 2-D empirical mode decomposition (2D-EMD), it is found that our proposed 2D-SSA approach generates the best results in most cases. Unlike 2D-EMD that requires sequential transforms to obtain detailed decomposition, 2D-SSA extracts all components simultaneously. As a result, the execution time in feature extraction can be also dramatically reduced. The superiority in terms of enhanced discrimination ability from 2D-SSA is further validated when a relatively weak classifier, i.e., the k-nearest neighbor, is used for data classification. In addition, the combination of 2D-SSA with 1-D principal component analysis (2D-SSA-PCA) has generated the best results among several other approaches, demonstrating the great potential in combining 2D-SSA with other approaches for effective spatial-spectral feature extraction and dimension reduction in HSI.

Hyperspectral reflectance images of healthy and diseased leaves infected with different isolates of Citrus tristeza virus (CTV) including TRL514, CT30, CT32 and CT11A were collected in the visible and near-infrared region of 400–1000 nm. Average reflectance spectrum was generated from each hyperspectral image individually obtained from 60 healthy and 240 CTV-infected leaves. The spectra were transformed with 15-point Savitzky Golay second derivative. Then principal component analysis was performed on the transformed data in order to reduce the dimension of data. Comparative analysis was performed among supervised classification models, including back-propagation neural network (BPNN), linear discriminant analysis (LDA) and Mahalanobis distance (MD). When the second derivative spectra were analyzed, classifier models including BPNN, LDA and MD can discriminate the healthy and CTV-infected leaves with the highest classification accuracies of 100% in the spectral range of 400–1000 nm and 760–1000 nm. Nine op...

Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics.

This work proposes a methodology for content-based image retrieval of glioblastoma multiforme (GBM) and non-GBM tumors. Regions containing GBM lesions from 40 patients and non-GBM lesions from 20 patients were manually segmented from MR imaging studies (T1 post-contrast and T2 weighted channels) to form the training set. In addition to the two acquired channels, a composite image was formed by an image fusion method. Data reduction techniques, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied on the training sets (T1 post, T2, composite, and multi-channel combining the PCA features from T1 post and T2). The retrieval accuracy was evaluated using a 'leave-one-out' strategy with query images belonging to 'normal', 'GBM' and 'non-GBM' classes. Several combinations of the similarity metric and classifier were used: Euclidean similarity measures with k-means classifier for the PCA and LDA features and support vector machine (SVM) nonlinear classifier (radial basis function kernel) with the PCA derived features. The SVM classifier served as a comparison of nonlinear techniques vs. linear ones. Multi-channel PCA was 100% accurate in classifying a query image as either 'normal' or 'abnormal'. The highest accuracy in classification of tumor grade (GBM or other Grade 3) was 77% and was achieved by SVM coupled with the PCA features. The proposed algorithm intent is to be integrated into an automated decision support system for MR brain tumor studies.

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.

In this paper, we present the principal component analysis (PCA) of shape deformations of bilateral hippocampi in Alzheimer's disease (AD) as derived in the large deformation diffeomorphic metric mapping setting. We investigated the PCA patterns (the scores and loadings) of the bilateral hippocampi for 51 subjects, 28 of which had AD while 23 were normal aging. Student's t-tests were used to select the components displaying significant group difference for a more purposed analysis. Our findings revealed that the head part of the hippocampus in each hemisphere, to be specific the CA1 and subiculum subregions, contributed the most to the significant group differences. To further our analysis, we examined the classification accuracy yielded by these shape deformation patterns when either solely PCA or PCA followed by a Student's t-test was utilized as the approach to dimension reduction. Linear discriminant analysis (LDA) and support vector machine (SVM) were used as candidates for the classification technique. According to our leave-one-out cross-validation experiments, SVM had a much higher accuracy than LDA, with the best performance (overall accuracy: 94.1% (48/51); sensitivity: 92.9% (26/28); and specificity: 95.7% (22/23)) achieved by SVM when using both the left and the right hippocampal shape deformation patterns and employing solely PCA for dimension reduction.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimension reduction algorithms, including principal component analysis (PCA), local preserving projections (LPP), neighborhood preserving embedding (NPE), and linear discriminant analysis (LDA), were used to reduce hyperspectral dimensions, and their classification performances on the algae concentration levels in water samples using hyperspectral imaging were compared. The LPP model showed satisfactory classification accuracy of 94.296 %, which was superior to the results based on reducing spectral dimensions with LDA (94.118 %), NPE (93.353 %), and PCA (90.588 %). The results demonstrated the potential of hyperspectral imaging coupled with dimension reduction methods in classifying water bodies with different algae concentration levels.

Hyperspectral image (HSI) data have large numbers of bands that probably not all bands are equally informative and predictive for an effective HSI classification. Effective algorithms are highly desired in many real-world HSI applications, especially in cases requiring rapid learning with limited computing power. To address the abovementioned case, we present in this article a novel plug-and-play compact band weighting (CBW) module based on the attention-driven mechanism that evaluates different spectral bands according to their contributions to a given classification task. Compared to existing band weighting (BW) modules with tens of thousands of network parameters by deep learning, the proposed CBW is a lightweight module with only 20 parameters. Both model complexity and time cost are significantly reduced. The CBW module implements BW by making full use of the correlation among the adjacent spectral bands and spectral statistic information and, thereby, leads to the effect of recalibrated HSI. The experimental study has been conducted on three widely used HSI data sets, and results show the superiority of the proposed algorithm over current state-of-the-art methods of BW. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JarvenYi/CBW</uri> .