- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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EXPRESS: Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra
摘要： The last two decades have seen tremendous progress in the application of two-dimensional correlation spectroscopy (2D-COS) as a versatile analysis method for data series obtained by a large variety of different spectroscopic modalities, including infrared (IR) and Raman spectroscopy. The analysis technique is applicable to a series of spectra recorded under the influence of an external sample perturbation. 2D-COS analysis is not only helpful to decipher correlations which may exist between distinct spectral features, but can be utilized also to obtain the sequence of individual spectral changes. The focus of this review article is on the application of 2D-COS for analyzing spatially resolved data with special emphasis on hyperspectral imaging (HSI) study. In this review, we briefly introduce the fundamentals of the generalized 2D-COS analysis approach, discuss specific points of 2D-COS application to spatially resolved spectra and demonstrate essential aspects of data pre-processing for 2D-COS analysis of spatially resolved spectra. Based on illustrative examples we show that 2D-COS is useful for spectral band assignment in HSI applications and demonstrate its utility for detecting subtle correlations between spectra features, or between features from different imaging modalities in the case of heterospectral (multimodal) HSI. Furthermore, a short overview on existing 2D-COS software tools is provided. It is hoped that this article represents not only a useful guideline for 2D-COS analyses of spatially resolved hyperspectral data but supports also further dissemination of the 2D-COS analysis method as a whole.
关键词： Spatially Resolved Vibrational Microspectroscopy,Multimodal Imaging,Two-Dimensional Correlation Spectroscopy (2D-COS),Hyperspectral Imaging
更新于2025-09-10 09:29:36
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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 - A New Unmixing-Based Approach for Shadow Correction of Hyperspectral Remote Sensing Data
摘要： Hyperspectral remote sensing data are widely used in various applications like classification and target detection. However, recently the influence of shadow has become increasingly greater due to the higher spatial resolution of such data. Shaded areas usually have lower intensity and fuzzy boundary, which make the images hard to interpret automatically. To overcome this issue, shadow correction/compensation of hyperspectral remote sensing data is one of the most used techniques. This process includes in general, the detection and de-shadowing steps. In this work, which focuses only on the de-shadowing step, a new hyperspectral unmixing-based shadow correction/compensation is presented. Experiments are conducted on a real hyperspectral image to evaluate the performance of the proposed approach. Experiments show that the proposed method yields satisfactory de-shadowing results and provides better overall performance compared to another unmixing-based method from the literature.
关键词： linear spectral unmixing,shadow correction/compensation,Hyperspectral imaging
更新于2025-09-10 09:29:36
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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 - A Novel Supervised Linear Spectral Unmixing Model Constrained Pso Approach for Abundance Estimation
摘要： The existence of mixed pixels is common in hyperspectral data. Although, the proportion of each spectral signature in a given mixed pixels scenario may be determined through the spectral unmixing operations. In this work, Particle Swarm Optimization (PSO) based approach is proposed in order to estimate the abundances fractions for spectral unmixing. It calculates the position in order to estimate the fractions. In this, the concept of particles per solution is omitted in order to do unmixing. Hence, each pixel of data is our particle, and the solution is its abundance fractions. This approach is less computationally complex as compared to other proposed PSO based approaches for abundance estimation. Herein, supervised linear mixing model and spatially correlated data are the assumptions considered for unmixing operation. The proposed method is tested on simulated data and it has been observed to be performing well.
关键词： Linear,PSO,Unmixing,Hyperspectral imaging
更新于2025-09-10 09:29:36
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Determination and Visualization of Different Levels of Deoxynivalenol in Bulk Wheat Kernels by Hyperspectral Imaging
摘要： A hyperspectral imaging system is proposed as a method to rapidly and nondestructively predict mycotoxin deoxynivalenol (DON) levels in FHB-infected wheat kernels. Standard normal variate transformation and multiplicative scatter correction (MSC) were used in spectral preprocessing. The successive projections algorithm (SPA) and random frog algorithm were used to select the optical wavelengths. Finally, the support vector machine (SVM) technique and partial least squares discriminant analysis were applied to establish different models for determining DON levels. Based on a comparison of the results, the MSC–SPA–SVM model, with the highest classi?cation accuracy (100.00% for the training test and 97.92% for the testing set), gave the best performance, and a visualization map of the DON content level based on this model was created.
关键词： optical wavelength selection,hyperspectral imaging,classi?cation model,deoxynivalenol content,bulk wheat kernels,visualization map
更新于2025-09-10 09:29:36
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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 - Structural Optimization For Accurate Characterization Of Urban Areas In Hyperspectral Datasets
摘要： Accurately estimating the urbanization process is a key-factor for the actual implementation of the sustainable development goals identified by transnational institutions and agencies. In order to retrieve precise characterization of the anthropogenic extents and a sound human-environment interaction assessment, the analysis of Earth observations (EOs) plays a crucial role. Especially, the use of nonlinear spectral investigation can improve the description of geometrically and morphologically complex scenes, so that anthropogenic settlements and dynamics can be properly outlined. In this paper, we propose a novel method for directly assessing the distribution of materials and elements in hyperspectral images by means of a structural optimization approach. Experimental results show how the proposed approach is able to deliver accurate and reliable characterization of urban materials and extents.
关键词： nonlinear spectral investigation,structural optimization,hyperspectral imaging,urban materials characterization
更新于2025-09-10 09:29:36
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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 - A Dataset with Ground-Truth for Hyperspectral Unmixing
摘要： Spectral unmixing is one of the most important issues of hyperspectral data processing. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms. In this work, we create several experimental scenes in our laboratory with controlled settings where the pure material spectra and material compositions are known. Lab-made hyperspectral datasets with these scenes are then provided, and mutually validated with typical linear and nonlinear unmixing algorithms.
关键词： unmixing database,spectral unmixing,Hyperspectral imaging,ground-truth
更新于2025-09-10 09:29:36
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Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification
摘要： In recent studies in hyperspectral imaging, biometrics, and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data are limited; therefore, hyperspectral imaging is one such example that benefits from this framework. Most of the prior studies were based on the unsupervised formulation; and in all cases, the training algorithm was greedy and hence suboptimal. This is the first work that shows how to learn the deep dictionary learning problem in a joint fashion. Moreover, we propose a new discriminative penalty to the said framework. The third contribution of this work is showing how to incorporate stochastic regularization techniques into the deep dictionary learning framework. Experimental results on hyperspectral image classification shows that the proposed technique excels over all state-of-the-art deep and shallow (traditional) learning based methods published in recent times.
关键词： hyperspectral imaging,dictionary learning,Classification,deep learning,supervised learning
更新于2025-09-10 09:29:36
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A combined chemical imaging approach using (MC) LA-ICP-MS and NIR-HSI to evaluate the diagenetic status of bone material for Sr isotope analysis
摘要： This paper presents a combination of elemental and isotopic spatial distribution imaging with near-infrared hyperspectral imaging (NIR-HSI) to evaluate the diagenetic status of skeletal remains. The aim is to assess how areas with biogenic n(87Sr)/n(86Sr) isotope-amount ratios may be identified in bone material, an important recorder complementary to teeth. Elemental (C, P, Ca, Sr) and isotopic (n(87Sr)/n(86Sr)) imaging were accomplished via laser ablation (LA) coupled in a split stream to a quadrupole inductively coupled plasma mass spectrometer (ICP-QMS) and a multicollector inductively coupled plasma mass spectrometer (MC ICP-MS) (abbreviation for the combined method LASS ICP-QMS/MC ICP-MS). Biogenic areas on the bone cross section, which remained unaltered by diagenetic processes, were localized using chemical indicators (I(C)/I(Ca) and I(C) × 10/I(P) intensity ratios) and NIR-HSI at a wavelength of 1410 nm to identify preserved collagen. The n(87Sr)/n(86Sr) isotope signature analyzed in these areas was in agreement with the biogenic bulk signal revealed by solubility profiling used as an independent method for validation. Elevated C intensities in the outer rim of the bone, caused by either precipitated secondary minerals or adsorbed humic materials, could be identified as indication for diagenetic alteration. These areas also show a different n(87Sr)/n(86Sr) isotopic composition. Therefore, the combination of NIR-HSI and LASS ICP-QMS/MC ICP-MS allows for the determination of preserved biogenic n(87Sr)/n(86Sr) isotope-amount ratios, if the original biogenic material has not been entirely replaced by diagenetic material.
关键词： LASS ICP-QMS/MC ICP-MS,Diagenesis,Human bone remains,Near-infrared hyperspectral imaging
更新于2025-09-10 09:29:36
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Integration of point cloud data and hyperspectral imaging as a data gathering methodology for refurbishment projects using building information modelling (BIM)
摘要： Purpose – Building information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of bene?ts in terms of achieving the ef?cient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and signi?cant bene?ts in terms of ef?ciency gains and cost savings should be achieved. Design/methodology/approach – Laser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building speci?cation and construction materials, to be in used in BIM. Findings – Hyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identi?cation and classi?cation of surface materials and would also convert the 3D representation to BIM. Originality/value – This integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the ef?ciency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the ?rst time in the context of buildings.
关键词： Laser scanning,Information modelling,Refurbishment,BIM,Point cloud,Hyperspectral imaging
更新于2025-09-10 09:29:36
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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 - Fusion of Lidar, Hyperspectral and RGB Data for Urban Land Use and Land Cover Classification
摘要： In this paper, we present an ensemble-based classification approach for urban land use and land cover classification based on multispectral LiDAR, hyperspectral and very high resolution RGB data. The approach has been evaluated on the dataset provided for the IEEE GRSS 2018 Data Fusion Contest organized by the GRSS IADF technical committee and has been proven to have a high operational performance, being able to distinguish between different grass-, building- and street-types among other classes like water, railways and parking lots as well as other non-typical classes like cars, trains, stadium seats, etc.
关键词： multispectral LiDAR,very high-resolution RGB,hyperspectral imaging,land use classification
更新于2025-09-10 09:29:36