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- 摘要
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- 实验方案
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Multispectral Airborne LiDAR Data in the Prediction of Boreal Tree Species Composition
摘要： Multispectral light detection and ranging (LiDAR) instruments, such as Optech Titan, record intensities at multiple wavelengths and these intensities can be used for tree species prediction in the same way as multispectral image data. In this paper, our main objective was to compare the accuracy of tree species prediction in a boreal forest area using multispectral LiDAR, the 1064-nm wavelength channel ('unispectral LiDAR'), and unispectral LiDAR with auxiliary aerial image data. We also evaluated the effect of the widely used intensity range correction method. We classified the main tree species of field plots using linear discriminant analysis (LDA) and predicted the species-specific volume proportions (%) for Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and broadleaved trees using the k-nearest neighbor imputation. The effect of intensity correction on prediction errors for the dominant tree species was evaluated using optimal parameters derived from: 1) minimal intensity difference between flight lines; 2) parameters suggested by theory; and 3) uncorrected data. Although the range correction increased the classification accuracy slightly, it was observed to be ambiguous, and not consistent with theory for canopy echoes. Regardless, the intensity values were useful for the prediction of dominant tree species and species' volume proportions. The results for the dominant tree species classification using multispectral LiDAR [overall accuracy (OA) 88.2%, kappa 0.79] were comparable to the use of unispectral LiDAR and aerial images (OA 89.1%, kappa 0.81). We conclude that the multispectral LiDAR may become a useful tool in operational species-specific forest inventories.
关键词： laser backscatter intensity,k-nearest neighbor (k-NN),Intensity correction,linear discriminant analysis (LDA),multispectral airborne laser scanning,tree species classification
更新于2025-09-23 15:22:29
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Rapid quality assessment of isogams using laser plasma spectroscopy
摘要： In this paper, the quality assessment of isogams is demonstrated by laser-induced breakdown spectroscopy (LIBS) using the comparative standardization method. Here, the mass concentrations of carbon and hydrogen, as basic elements of tar, relative to that of calcium, as an undesired element, are taken into account as principal parameters to determine the quality of isogams. Hence, the intensity ratios of H?? line of hydrogen (656.28?nm), the (0, 0) band of CN (388.34?nm), and the (0, 0) band of C2 (516.52?nm) to the line intensity of once-ionized calcium (317.93?nm) are considered as determinant markers for five different pre-known isogam brands. Qualitatively, classification of the isogams based on this approach is in full agreement with that obtained from the results of Fourier-transform infrared (FTIR) spectroscopy. In FTIR spectra, two stronger transitions of 2849?cm?1 and 2917?cm?1 related to the symmetric and asymmetric stretching vibrations of C–H play the principal role in the analysis of samples. Furthermore, the results obtained from energy-dispersive X-ray (EDX) analysis quantitatively confirm the LIBS outcomes. And finally, to reveal the differences between isogams from various aspects, the linear discriminant analysis (LDA) is exploited as a statistical approach.
关键词： FTIR spectroscopy,EDX analysis,Linear discriminant analysis (LDA),Laser-induced breakdown spectroscopy (LIBS),Isogams,Quality assessment
更新于2025-09-23 15:21:01
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Identification of Gravesa?? ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method
摘要： Diagnosis of the Graves’ ophthalmology remains a significant challenge. We identified between Graves’ ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves’ ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves’ ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves’ ophthalmology.
关键词： support vector machine (SVM),linear discriminant analysis (LDA),Graves’ ophthalmology,laser-induced breakdown spectroscopy (LIBS),k-nearest neighbor (kNN),generalized regression neural network (GRNN)
更新于2025-09-23 15:21:01
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Locally Weighted Discriminant Analysis for Hyperspectral Image Classification
摘要： A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20% for Indian Pines and 17% for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.
关键词： hyperspectral image (HSI) classification,linear discriminant analysis (LDA),spatial-spectral information,dimensionality reduction
更新于2025-09-19 17:15:36
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Classification of Ceramic Tableware by Laser Induced Breakdown Spectroscopy and Chemometrics
摘要： Ceramics manufactured in Brazil and in other countries were analyzed by laser induced breakdown spectroscopy (LIBS). The LIBS spectra were employed to identify the origin of the tableware as produced in Brazil or abroad using soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA). Three sets of spectral variables were evaluated aiming at discrimination of the ceramics: the entire LIBS spectrum, a set of selected spectral ranges, and the maximum of emission intensity of the elements Al, Ca, Mg, Si, Na and Ti. The results for the three sets of variables show good performance, achieving correct classification from 88.6 to 94.3%, and 86.7 to 100% for the external training and test, respectively.
关键词： laser induced breakdown spectroscopy (LIBS),chemometrics,soft independent modeling of class analogy (SIMCA),linear discriminant analysis (LDA),Ceramic tableware classification
更新于2025-09-12 10:27:22