- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- hyperspectral images
- spectral and spatial features
- classification
- SVM
- mutual information
- GLCM
- Optoelectronic Information Science and Engineering
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
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Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages
摘要： Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350–1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460–720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568–709 nm and 725–1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R2 of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R2 of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage.
关键词： yellow rust disease,three-band spectral index,different growth stages,hyperspectral remote sensing,wheat infection
更新于2025-09-23 15:22:29
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Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm
摘要： Hyperspectral imaging technology with sufficiently discriminative spectral and spatial information brings new opportunities for robust facial image recognition. However, hyperspectral imaging poses several challenges including a low signal-to-noise ratio (SNR), intra-person misalignment of wavelength bands, and a high data dimensionality. Many studies have proven that both global and local facial features play an important role in face recognition. This research proposed a novel local features extraction algorithm for hyperspectral facial images using local patch based low-rank tensor decomposition that also preserves the neighborhood relationship and spectral dimension information. Additionally, global contour features were extracted using the polar discrete fast Fourier transform (PFFT) algorithm, which addresses many challenges relevant to human face recognition such as illumination, expression, asymmetrical (orientation), and aging changes. Furthermore, an ensemble classifier was developed by combining the obtained local and global features. The proposed method was evaluated by using the Poly-U Database and was compared with other existing hyperspectral face recognition algorithms. The illustrative numerical results demonstrate that the proposed algorithm is competitive with the best CRC_RLS and PLS methods.
关键词： spectral and spatial information,polar discrete fast Fourier transform,band fusion,ensemble classifier,global and local features,tensor decomposition,hyperspectral images
更新于2025-09-23 15:22:29
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Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution
摘要： Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatial-resolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensor-train (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes. First, we cluster the HR-MSI cubes as many groups based on their similarities, and the HR-HSI cubes are also clustered according to the learned cluster structure in the HR-MSI cubes. The HR-HSI cubes in each group are much similar to each other and can constitute a 4-D tensor, whose four modes are highly correlated. Therefore, we impose the LTTR constraint on these 4-D tensors, which can effectively learn the correlations among the spatial, spectral, and nonlocal modes because of the well-balanced matricization scheme of TT rank. We formulate the super-resolution problem as TT rank regularized optimization problem, which is solved via the scheme of alternating direction method of multipliers. Experiments on HSI data sets indicate the effectiveness of the LTTR-based method.
关键词： low tensor-train (TT) rank (LTTR) learning,image fusion,Hyperspectral imaging,superresolution
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hyperspectral Image Classification VIA a Joint Sparsity and Spatial Correlation Model
摘要： In this paper, a novel constrained Sparse Representation (SR) algorithm based on the joint sparsity and spatial correlation for hyperspectral image (HSI) classification is proposed. The coefficients in the sparse vector associated with the training samples in the structured dictionary exhibit the group sparsity continuity. However, this joint sparsity of the coefficient vector is not considered in the classical SR classifiers. In addition, spatial correlation has positive effect on HSI classification processing. Thus in the proposed SR model, we consider a joint sparsity regularization term to promote the joint sparsity of the sparse vectors and use space regularization to restrict spatial correlation of the output. The formulated problem is solved via the alternating direction method of multipliers (ADMM). Simulation results show that the proposed algorithm has the improved performance.
关键词： sparse representation,classification,Hyperspectral imagery,joint sparsity,ADMM
更新于2025-09-23 15:22:29
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A Coarse-to-Fine Optimization for Hyperspectral Band Selection
摘要： Hyperspectral band selection is a feature selection method that selects a most representative set of bands to achieve a good performance in several tasks such as classification and anomaly detection. It reduces the burden of storage, transmission, and computation. In this letter, a two-stage band selection algorithm is introduced. It selects bands and refines the result using a linear reconstruction error criterion. Then a coarse-to-fine band selection (CFBS) strategy is applied to the two-stage band selection in order to achieve a better result. CFBS selects bands group by group. Each group is selected based on bands that are not well represented by the previous groups, trying to minimize the linear reconstruction error. Experiments show that the proposed method has a significant advancement compared with other competitors.
关键词： Band selection,hyperspectral imaging.
更新于2025-09-23 15:22:29
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A Nondestructive Real-Time Detection Method of Total Viable Count in Pork by Hyperspectral Imaging Technique
摘要： A nondestructive method was developed for assessing total viable count (TVC) in pork during refrigerated storage by using hyperspectral imaging technique in this study. The hyperspectral images in the visible/near-infrared (VIS/NIR) region of 400–1100 nm were acquired for fifty pork samples, and their VIS/NIR diffuse reflectance spectra were extracted from the images. The reference values of TVC in pork samples were determined by classical microbiological plating method. Both partial least square regression (PLSR) model and support vector machine regression model (SVR) of TVC were built for comparative analysis to achieve better results. Different transformation methods and filtering methods were applied to improve the models. The results show that both the optimized PLSR model and SVR model can predict the TVC very well, while the SVR model based on second derivation was better, which achieved with RP (correlation coefficient of prediction set) = 0.94 and SEP (standard error of prediction set) = 0.4570 log CFU/g in the prediction set. An image processing algorithm was then developed to transfer the prediction model to every pixel of the image of the entire sample; the visualizing map of TVC would be displayed in real-time during the detection process due to the simplicity of the model. The results demonstrated that hyperspectral imaging is a potential reliable approach for non-destructive and real-time prediction of TVC in pork.
关键词： visible/near-infrared,total viable count,pork,hyperspectral imaging
更新于2025-09-23 15:22:29
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Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery
摘要： Dimensionality Reduction (DR) models are of signi?cance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph-Embedding Discriminant Analysis framework has demonstrated its effectiveness for HSI feature reduction. Based on this framework, many representation based models are developed to learn the similarity graphs, but most of these methods ignore the spatial information, resulting in unsatisfactory performance of DR models. In this paper, we ?rstly propose a novel supervised DR algorithm termed Spatial-aware Collaborative Graph for Discriminant Analysis (SaCGDA) by introducing a simple but ef?cient spatial constraint into Collaborative Graph-based Discriminate Analysis (CGDA) which is inspired by recently developed Spatial-aware Collaborative Representation (SaCR). In order to make the representation of samples on the data manifold smoother, i.e., similar pixels share similar representations, we further add the spectral Laplacian regularization and propose the Laplacian regularized SaCGDA (LapSaCGDA), where the two spectral and spatial constraints can exploit the intrinsic geometric structures embedded in HSIs ef?ciently. Experiments on three HSIs data sets verify that the proposed SaCGDA and LapSaCGDA outperform other state-of-the-art methods.
关键词： hyperspectral imagery,graph embedding,dimensionality reduction,collaborative representation,discriminant analysis
更新于2025-09-23 15:22:29
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Local Adaptive Joint Sparse Representation for Hyperspectral Image Classification
摘要： In this paper, a local adaptive joint sparse representation (LAJSR) model is proposed for the classification of hyperspectral remote sensing images. It improves the original joint sparse representation (JSR) method in both the signal and dictionary construction phase and sparse representation phase. Given a testing pixel, a similar signal set is constructed by picking a few of the most similar pixels from its spatial neighborhood. The original training dictionary consists of training samples from different classes and is extended by adding spatial neighbors of each training sample. A local adaptive dictionary is built by selecting the most representative atoms from the extended dictionary that are correlated to the similar signal set. In the LAJSR framework, the selected similar signals are simultaneously represented by the local adaptive dictionary, and the obtained sparse representation coefficients are further weighted by a sparsity concentration index vector which aims to concentrate and highlight the coefficients on the expected class. Experimental results on two benchmark hyperspectral datasets have demonstrated that the proposed LAJSR method is much more effective than existing JSR and SVM methods, especially in the case of small sample sizes.
关键词： local adaptive dictionary,hyperspectral image,Classification,joint sparse representation
更新于2025-09-23 15:22:29
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Spectral–spatial classification of hyperspectral images by algebraic multigrid based multiscale information fusion
摘要： In this work, we present a novel spectral-spatial classification framework of hyperspectral images (HSIs) by integrating the techniques of algebraic multigrid (AMG), hierarchical segmentation (HSEG) and Markov random field (MRF). The proposed framework manifests two main contributions. First, an effective HSI segmentation method is developed by combining the AMG-based marker selection approach and the conventional HSEG algorithm to construct a set of unsupervised segmentation maps in multiple scales. To improve the computational efficiency, the fast Fish Markov selector (FMS) algorithm is exploited for feature selection before image segmentation. Second, an improved MRF energy function is proposed for multiscale information fusion (MIF) by considering both spatial and inter-scale contextual information. Experiments were performed using two airborne HSIs to evaluate the performance of the proposed framework in comparison with several popular classification methods. The experimental results demonstrated that the proposed framework can provide superior performance in terms of both qualitative and quantitative analysis.
关键词： hierarchical segmentation,algebraic multigrid,hyperspectral images,spectral-spatial classification,multiscale information fusion,Markov random field
更新于2025-09-23 15:22:29
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A Novel Regularized Nonnegative Matrix Factorization for Spectral-Spatial Dimension Reduction of Hyperspectral Imagery
摘要： Dimension reduction (DR) is an essential preprocessing for hyperspectral image (HSI) classification. Recently, nonnegative matrix factorization (NMF) has been shown as an effective tool for the DR of hyperspectral data given the fact that it provides interpretable results. However, the basic NMF ignores the geometric structure information of the HSI data, thus limiting its performance. To this end, a novel regularized NMF method, termed NMF with adaptive graph regularizer (NMFAGR), is proposed for the spectral-spatial dimension reduction of hyperspectral data in this paper. Specifically, to enhance the preservation ability of the geometric structure information, the NMFAGR performs the dimension reduction and graph learning simultaneously. Regarding the mutual correlation between these two tasks, a graph regularizer is added as an interaction. Moreover, to effectively utilize complementary information among spectral-spatial features, the NMFAGR allocates feature weight factors automatically without requiring any additional parameters. An efficient algorithm is utilized to solve the optimization problem. The effectiveness of the proposed method is demonstrated on three benchmark hyperspectral data sets through experimentation.
关键词： Hyperspectral images,feature extraction,pattern recognition
更新于2025-09-23 15:22:29