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- 实验方案
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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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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Image Super-Resolution via Local Low-Rank and Sparse Representations
摘要： Remotely sensed hyperspectral images (HSIs) usually have high spectral resolution but low spatial resolution. A way to increase the spatial resolution of HSIs is to solve a fusion inverse problem, which fuses a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) of the same scene. In this paper, we propose a novel HSI super-resolution approach (called LRSR), which formulates the fusion problem as the estimation of a spectral dictionary from the LR-HSI and the respective regression coefficients from both images. The regression coefficients are estimated by formulating a variational regularization problem which promotes local (in the spatial sense) low-rank and sparse regression coefficients. The local regions, where the spectral vectors are low-rank, are estimated by segmenting the HR-MSI. The formulated convex optimization is solved with SALSA. Experiments provide evidence that LRSR is competitive with respect to the state-of-the-art methods.
关键词： Hyperspectral image super-resolution,low rank,superpixels
更新于2025-09-23 15:22:29
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Predicting Apple Firmness and Soluble Solids Content Based on Hyperspectral Scattering Imaging Using Fourier Series Expansion
摘要： This article reports on using a Fourier series expansion method to extract features from hyperspectral scattering profiles for apple fruit firmness and soluble solids content (SSC) prediction. Hyperspectral scattering images of ‘Golden Delicious’ (GD), ‘Jonagold’ (JG), and ‘Delicious’ (RD) apples, harvested in 2009 and 2010, were acquired using an online hyperspectral imaging system over the wavelength region of 500 to 1000 nm. The moment method and Fourier series expansion method were used to analyze the scattering profiles of apples. The zeroth-first order moment (Z-FOM) spectra and Fourier coefficients were extracted from each apple, which were then used for developing fruit firmness and SSC prediction models using partial least squares (PLS) and least squares support vector machine (LSSVM). The PLS models based on the Fourier coefficients improved the standard errors of prediction (SEP) by 4.8% to 19.9% for firmness and by 2.4% to 13.5% for SSC, compared with the PLS models using the Z-FOM spectra. The LSSVM models for the prediction set of Fourier coefficients achieved better SEP results, with improvements of 4.4% to 11.3% for firmness and 2.8% to 16.5% for SSC over the LSSVM models for the Z-FOM spectra data and 3.7% to 12.6% for firmness and 5.4% to 8.6% for SSC over the PLS models for the Fourier coefficients. Experiments showed that Fourier series expansion provides a simple, fast, and effective means for improving hyperspectral scattering prediction of fruit internal quality when used with either PLS or LSSVM.
关键词： Partial least squares,Soluble solids content,Apples,Least squares support vector machine,Fourier series expansion,Hyperspectral scattering imaging,Firmness
更新于2025-09-23 15:22:29
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Leaf Quality :Hyperspectral Imaging Technology
摘要： India is a rural nation 70% of the populace relies upon horticulture. Farmers are the backbone of our nation.70% of the economic growth depends on agriculture. The major food crops of India are wheat, corn, rice, barley, sorghum. In this project the concentration is on paddy as it is a main food crop of our state. This project helps us to find whether the leaf is diseased or not and also helps us to find the type of disease in paddy leaf. The agribusiness research of programmed leaf sickness recognition is fundamental research point as it might demonstrate benefits in checking substantial fields of products, and subsequently naturally recognize manifestations of illness when they show up on plant takes off. Digital image processing Advanced is a procedure utilized for improvement of the picture. To enhance farming items programmed recognition of side effects is useful.
关键词： Lesions,Bhattacharya Algorithm,Bacterial Disease,Burning Disease,HSI,Hyperspectral
更新于2025-09-23 15:22:29
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Separable-spectral convolution and inception network for hyperspectral image super-resolution
摘要： Due to the limitation of the imaging system, it is hard to get Hyperspectral Image (HSI) with very high spatial resolution. Super-Resolution (SR) is a handling missing data technology to restore high-frequency information from the low-resolution image, can be used to solve this problem. Recently, Deep Learning (DL) has achieved great performance in computer vision, including SR. However, most DL-based HSI SR methods neglect the spectral disorder caused by normal 2D convolution. This paper proposes a novel end–end deep learning-based network named Separable-Spectral and Inception Network (SSIN) for HSI SR. In SSIN, the feature extraction module independently extracts features of each band image, and then these features are fused together to further exploit residual image by using feature fusion module. In reconstruction module, a multi-path connection is built to obtain features of different levels to restore high spatial resolution image in a coarse-to-fine manner. Experiments are implemented on two datasets include both indoor and airborne HSIs, and the performances of SSIN are evaluated in different conditions. Experimental results show that adding several separable spectral convolutions and multi-path connection in a deep network can greatly improve the SR performance, and SSIN achieves higher accuracy and better visualization compare with other methods.
关键词： Hyperspectral Image,Separable-spectral convolution,Deep learning,Super-resolution,Multi-path reconstruction
更新于2025-09-23 15:22:29
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Proximal VIS-NIR spectrometry to retrieve substance concentrations in surface waters using partial least squares modelling
摘要： Many water quality parameters such as concentrations of suspended matter, nutrients and algae directly or indirectly change the electromagnetic reflectance and transmission properties of surface water bodies. Optical measurement approaches have shown great potential to partially substitute water sampling and laboratory analyses, but are obstructed by limited flexibility or high maintenance demands. In order to overcome these problems and to bridge the gap between in situ and remote sensing measurements, the use of close-range, above-surface reflectance measurements in the VIS-NIR domain to measure water quality parameters in surface water bodies was investigated. Remote sensing reflectance in a 1 m3 water tank with increasing, known concentrations of suspended solids was measured. A partial least squares model was trained to predict concentrations from reflectance curves, which performed well, considering the wide range of concentrations and illumination conditions (R2cal ? 0.96, R2val ? 0.97). The approach was then transferred to the field and further parameters were tested. Using a semi-autonomous spectrometer mounted to a boom stand on a motor boat, we traced substance concentrations in close intervals along a longitudinal gradient from inflow to dam in a drinking water reservoir in Brazil. The method is suitable for parameters directly influencing the reflection properties of the water body (e.g. suspended solids (R2cal ? 0.93), chlorophyll-a (R2cal ? 0.74)), or for parameters closely related to those (e.g. total phosphorus (R2cal ? 0.97)). For chemical oxygen demand, the method is not well suited (R2cal ? 0.14, R2val ? 0.45). Once calibrated to the local conditions, the spectrometer can be used stationary or on moving platforms to map and monitor surface waters. The integration of the procedure into acoustic and imaging techniques is further investigated.
关键词： water quality,suspended solids,hyperspectral,reservoir,partial least squares,proximal sensing
更新于2025-09-23 15:22:29
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Hyperspectral and Color Imaging of Solvent Vapor Sorption Into Porous Silicon
摘要： A porous silicon thin film photonic crystal (rugate) sample with both a radial gradient in the rugate reflectance band wavelength and two spatially separated pore-wall surface chemistries (methylated and oxidized) was monitored by hyperspectral and color imaging while it was dosed with vapors of acetone, ethanol, heptane, 2-propanol, and toluene at concentrations ranging from 100 to 3,000 mg m?3. The shift in the wavelength of the rugate reflectance band maximum at each position along a transect across the two surface chemistries, as derived from the hyperspectral imaging, could discriminate between the different solvents and concentrations of solvents, while the change in hue derived from the color camera data along an analogous transect did not provide discrimination. The discrimination between solvents was mainly due to the two different surface chemistries, and the gradient associated with the change in the rugate reflectance band wavelength did not affect the selectivity significantly. There was spatial variability in the spectral and color responses along the transect independent of the overall rugate reflectance band wavelength gradient and pore-wall surface chemistries, and this was attributed to factors such as the presence of striations in the silicon wafer from which the porous silicon was prepared.
关键词： sensor,porous silicon,hyperspectral imaging,surface modification,vapor sensing
更新于2025-09-23 15:22:29
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Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms
摘要： The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.
关键词： spectral tracking,time series,plant phenotyping,hyperspectral imaging,disease detection
更新于2025-09-23 15:22:29
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Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging
摘要： Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine?SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.
关键词： hyperspectral imaging technology,standard germination tests,support vector machine model,accelerated aging,principal component analysis,maize kernel
更新于2025-09-23 15:22:29
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High Throughput AOTF Hyperspectral Imager for Randomly Polarized Light
摘要： The acousto-optic tunable filter (AOTF) is one of the most used techniques for hyperspectral imaging (HSI), and is capable of fast and random wavelength access, high diffraction efficiency, and good spectral resolution. Typical AOTF-HSI works with linearly polarized light; hence, its throughput is limited for randomly polarized applications such as fluorescence imaging. We report an AOTF-based imager design using both polarized components of the input light. The imager is designed to operate in the 450 to 800 nm region with resolutions in the range of 1.5–4 nm. The performance characterization results show that this design leads to 68% improvement in throughput for randomly polarized light. We also compared its performance against a liquid crystal tunable filter (LCTF)-based imager.
关键词： hyperspectral imaging,LCTF,AOTF,optical throughput
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Blind Nonlinear Hyperspectral Unmixing Using an <tex>$\ell_{q}$</tex> Regularizer
摘要： Hyperspectral unmixing consists of estimating pure material spectra (endmembers) and their corresponding abundances in hyperspectral images. In this paper, a blind nonlinear hyperspectral unmixing algorithm is presented. The algorithm promotes sparse abundance maps using an lq regularizer and assumes that the spectra are mixed according to an extension to generalized bilinear model, called the Fan model. The algorithm is evaluated using both simulated and real hyperspectral data.
关键词： non-negative matrix factorization,Spectral unmixing,bilinear model,hyperspectral images
更新于2025-09-23 15:22:29