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- 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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Building a Hyperspectral Library and its Incorporation into Sparse Unmixing for Mineral Identification
摘要： The objective of the SOLSA project (EU-H2020) is to develop an analytical expert system for on-line-on-mine-real-time mineralogical and geochemical analyses on sonic drill cores. As one aspect of the system, this paper presents the building of the hyperspectral library and its incorporation into sparse unmixing techniques for mineral identification. Twenty seven spectra representing 14 minerals have been collected for the library. Three sparse unmixing techniques have been investigated and evaluated using simulated data generated from our hyperspectral library, and real hyperspectral data acquired from a serpentinized harzburgite sample. Among the three techniques, the collaborative sparse unmixing by variable splitting and augmented Lagrangian (CLSUnSAL) method provided the best accurate results on the simulated data. In addition, the results of the CLSUnSAL method show high correlation with that of the QEMSCAN? analysis on the harzburgite hyperspectral data.
关键词： Hyperspectral library,sparse unmixing,shortwave infrared (SWIR)
更新于2025-09-04 15:30:14
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Hyperspectral Anomaly Detection Using Collaborative Representation With Outlier Removal
摘要： Recently, collaborative representation detector (CRD) has been popularly used for hyperspectral anomaly detection. For the original CRD, the least squares solution becomes more unstable when more classes, i.e., samples for anomaly detection are involved, and the detection error is likely to happen if the test pixel is an anomalous pixel and several samples from background are similar anomalous. In this paper, we propose a hyperspectral anomaly detection method that uses CRD with principal component analysis (PCA) for removing outlier (PCAroCRD). According to the different background modeling methods, global and local versions are proposed. In the proposed algorithm, the spatial-domain PCA is adopted to extract main pixel information of global/local background that will be used as samples for collaborative representation, and simultaneously the information of abnormal pixels in global/local background can be removed. Fewer useful samples can also keep the detection result stable. Experimental results indicate that the PCAroCRD outperforms the original CRD, kernel version of CRD, advanced CRD (CRDBORAD), morphology-based CRD, Global Reed–Xiaoli (RX) algorithm, and the Local RX.
关键词： hyperspectral imagery,target detection,collaborative representation (CR),PCA,Anomaly detection
更新于2025-09-04 15:30:14
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Differentiation of Deciduous-Calyx and Persistent-Calyx Pears Using NIR Hyperspectral Imaging Analysis
摘要： Korla Fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant aroma after which they are named. The flesh of deciduous-calyx pears is considered more desirable in taste and texture attributes than that of persistent-calyx pears; Chinese packaging standards require that each packed case of highly demanded “superior” class pears contain 5% or less of the persistent-calyx fruits. Near-infrared hyperspectral imaging was investigated as a potential method for automatic sorting of the two types of pears. The hyperspectral images were analyzed, and wavebands at 1190 nm and 1199 nm were selected for differentiating deciduous-calyx fruits from persistent-calyx fruits. A multispectral differentiation algorithm using the ratio of the pears’ relative intensities at 1190 nm and 1199 nm was developed. The results showed that the algorithm correctly classified 89.3% to 94.0% of deciduous-calyx pears and effectively differentiated pears such that the number of persistent-calyx pears misclassified as deciduous-calyx pears comprised only 2.4% to 4.9% of all pears classified as deciduous-calyx pears, performing well within the targeted packaging standards.
关键词： Classification,Korla fragrant pear,Hyperspectral image,Fruit quality
更新于2025-09-04 15:30:14
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Development of a radiative transfer model for the determination of toxic gases by Fourier transform–infrared spectroscopy with a support vector machine algorithm
摘要： This report describes a radiative transfer model for Fourier transform-infrared (FT-IR) spectroscopy to create close-to-reality toxic gas spectra by reflecting the unique spectral responses of detectors and using the atmospheric radiative transfer code, MODTRAN. This system can be highly useful in overcoming the limitations for measuring toxic gases in open environments. The emulated gas spectra can be used to train support vector machine (SVM) for chemical gas detection. Its detection performance is evaluated with nerve agents (tabun, sarin, soman, and cyclosarin) and a simulant gas (sulfur hexafluoride) for indoor and outdoor experiments by using two off-the-shelf FT-IR gas detectors. The experimental results show that the proposed SVM algorithm successfully detected and classified targeted gases while reducing false negative and false positive detection rates.
关键词： support vector machine gas detection,Fourier transform infrared remote sensing,support vector machine,hyperspectral imaging,Fourier transform – infrared spectroscopy,stand-off detection
更新于2025-09-04 15:30:14
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Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering
摘要： An assessment of mineral identification applying hyperspectral infrared imagery in laboratory conditions is presented here and strives to identify nine different minerals (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, quartz). A hyperspectral camera in Long-Wave Infrared (LWIR, 7.7–11.8 μm) with a LW-macro lens, an infragold plate, and a heating source are instruments used in the experiment. For automated identification, a Sparse Principal Component Analysis (Sparse PCA)-based K-means clustering is employed to categorise all pixel-spectra in different groups. Then the best representatives of each cluster (using spectral averaging) are chosen to compare these spectra to ASTER spectral library of JPL/NASA through spectral comparison techniques. Spectral angle mapper (SAM) and Normalized Cross Correlation (NCC) are two of such techniques, which are used herein to measure the spectral difference. In order to evaluate robustness of clustering results among the minerals spectra, we have added three levels of Gaussian and salt&pepper noise, 0%; 2%, and 4%, to input spectra which dropped the accuracy percentage from more than 84.73%, for 0% added noise, to 44.57%, for 2% average of both additive noise, and 22.21%, for 4% additive noise. The results conclusively indicate a promising performance but noise sensitive behaviour of the proposed approach.
关键词： mineral identification,Hyperspectral imagery,sparse principle components analysis,clustering
更新于2025-09-04 15:30:14
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[Methods in Molecular Biology] BCL-2 Family Proteins Volume 1877 (Methods and Protocols) || Rapid Imaging of BCL-2 Family Interactions in Live Cells Using FLIM-FRET
摘要： The Bcl-2 proteins control cell death via interchanging interactions within the Bcl-2 family. Fluorescence lifetime imaging microscopy (FLIM) is used to detect F?rster resonance energy transfer (FRET) between two fluorescent-fusion proteins in live cells. FLIM-FRET has been used to detect specific interactions and their disruption, for Bcl-2 family proteins. To date, this has been possible only in low throughput, due to the time required for serial data acquisition. We developed an automated optical system with eight parallel detectors for rapid and efficient data collection. Here we describe how to use this system for FLIM-FRET imaging of Bcl-2 family protein interactions in a 384-well plate format.
关键词： BH3 mimetic,FLIM Hyperspectral,FLIM-FRET,Bcl-2 family,mCerulean3,High throughput,Fluorescence lifetime imaging microscopy
更新于2025-09-04 15:30:14
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou, China (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hyperspectral Target Detection Based on a Spatially Regularized Sparse Representation
摘要： Sparse representation (SR) is an effective method for target detection in hyperspectral imagery (HSI). The structured dictionary is arranged according to the target class and the background class, the sparse coefficients associated with each dictionary element of a given test sample can be recovered by solving an (cid:2)1-norm minimization problem. It is possible to introduce further regularization to improve the detection performance. The classical SR detection algorithms does not consider the spatial information of the detected pixels. It can be expected that sparse coefficients of adjacent pixels are similar due to the spatial correlation. This paper proposes a novel SR model which takes into account a spatial regularization term to promote the piecewise continuity of the sparse vectors. The formulated problem is solved via alternating direction method of multipliers (ADMM). We illustrate the enhanced performance of the proposed algorithm via both synthetic and real hyperspectral data.
关键词： spatial correlation constraint,sparse representation,ADMM,target detection,Hyperspectral imagery
更新于2025-09-04 15:30:14
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Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning
摘要： In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this article also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available datasets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.
关键词： hyperspectral images classification,Deep learning,spatial features,convolution neural network,minimum noise fraction
更新于2025-09-04 15:30:14
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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 - Onshore Hydrocarbon Remote Sensing
摘要： Hydrocarbon detection is important for both environment monitoring and hydrocarbon exploration. Hyperspectral imaging and derived spectral indices are used to detect hydrocarbons. With appropriate indices, light hydrocarbons on bare ground are detected. Heavier hydrocarbons are more difficult to detect. Plastic items are very well detected. Shadows and vegetation are generating some false alarms. Detection of hydrocarbon in urban environment, or on bare soils will be possible using spectral indices while detection of hydrocarbon in remote vegetated country areas will be difficult.
关键词： oil spill detection,Hydrocarbon index,hyperspectral imaging,multispectral imaging
更新于2025-09-04 15:30:14
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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 - Combining Deep and Shallow Neural Networks with Ad Hoc Detectors for the Classification of Complex Multi-Modal Urban Scenes
摘要： This article describes the work?ow of the classi?cation algorithm which ranked at 2nd place in the 2018 GRSS Data Fusion Contest. The objective of the contest was to provide a classi?cation map with 20 classes on a complex urban scenario. The available multi-modal data were acquired from hyperspectral, LiDAR and very high-resolution RGB sensors ?own on the same platform over the city of Houston, TX, USA. The classi?cation was obtained by merging deep convolutional and shallow fully-connected neural networks on a simpli?ed set of classes, complemented by a series of speci?c detectors and adhoc classi?ers.
关键词： hyperspectral,very high-resolution,data fusion,classi?cation,LiDAR
更新于2025-09-04 15:30:14