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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery II"

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A Pigment Analysis Tool for hyperspectral Images of Cultural Heritage Artifacts 23

A Pigment Analysis Tool for Hyperspectral Images of Cultural...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Bai, Di Messinger, David W. Howell, David Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Univ Oxford Bodleian Lib Broad St Oxford OX1 3BG England

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

The Gough Map, in the collection at the Bodleian Library, Oxford University, is one of the earliest surviving maps of Britain. Previous research deemed that it was likely created over the 15th century and afterwards it was extensively revised more than once. In 2015, the Gough Map was imaged using a hyperspectral imaging system at the Bodleian Library. The collection of the hyperspectral image (HSI) data was aimed at faded text enhancement for reading and pigment analysis for the material diversity of its composition and potentially the timeline of its creation. In this research, we introduce several methods to analyze the green pigments in the Gough Map, especially the number and spatial distribution of distinct green pigments. One approach, called the Gram Matrix, has been used to estimate the material diversity in a scene (i.e., endmember selection and dimensionality estimation). Here, we use the Gram Matrix technique to study the within-material differences of pigments in the Gough map with common visual color. We develop a pigment analysis tool that extracts visually common pixels, green pigments in this case, from the Gough Map and estimates its material diversity. It reveals that the Gough Map consists of at least six kinds of dominant green pigments. Both historical geographers and cartographic historians will benefit from this work to analyze the pigment diversity using HSI of cultural heritage artifacts.

关键词： hyperspectral imagery pigment analysis dimensionality estimation

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UAV imagery Analysis: Challenges and Opportunities 2

UAV Imagery Analysis: Challenges and Opportunities

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conference on Long-Range Imaging ii

作者： Grant, Barbara G. Grant Drone Solut LLC POB 1373 Cupertino CA 95015 USA

ISBN: (数字)9781510609105

ISBN: (纸本)9781510609099;9781510609105

As UAV imaging continues to expand, so too do the opportunities for improvements in data analysis. These opportunities, in turn, present their own challenges including the need for real time radiometric and spectral calibration;the continued development of quality metrics facilitating exploitation of strategic and tactical imagery;and the need to correct for sensor and platform-induced artifacts in image data.

关键词： imagery analysis radiometric calibration UAV imagers mapping UAS GSD commercial drone UAV community NDVI precision agriculture multispectral imager hyperspectral imager FAA Part 107 NiiRS GIQE

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On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery 23

On the creation of high spatial resolution imaging spectrosc...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Yao, Wei van Aardt, Jan Messinger, David Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

benefit of especially ecosystem studies. The onboard spectrometer will collect radiance spectra from the visible to short wave infrared (VSWIR) regions (400-2500 nm). The mission calls for fine spectral resolution (10 nm band width) and as such will enable scientists to perform material characterization, species classification, and even sub-pixel mapping. However, the global coverage requirement results in a relatively low spatial resolution (GSD 30m), which restricts applications to objects of similar scales. We therefore have focused on the assessment of sub pixel vegetation structure from spectroscopy data in past studies. In this study, we investigate the development or reconstruction of higher spatial resolution imaging spectroscopy data via fusion of multi-temporal data sets to address the drawbacks implicit in low spatial resolution imagery. The projected temporal resolution of the HyspIRI VSWIR instrument is 15 days, which implies that we have access to as many as six data sets for an area over the course of a growth season. Previous studies have shown that select vegetation structural parameters, e.g., leaf area index (LAI) and gross ecosystem production (GEP), are relatively constant in summer and winter for temperate forests;we therefore consider the data sets collected in summer to be from a similar, stable forest structure. The first step, prior to fusion, involves registration of the multi-temporal data. A data fusion algorithm then can be applied to the pre-processed data sets. The approach hinges on an algorithm that has been widely applied to fuse RGB images. Ideally, if we have four images of a scene which all meet the following requirements- i) they are captured with the same camera configurations;ii) the pixel size of each image is x;and iii) at least r(2) images are aligned on a grid of x/r- then a high-resolution image, with a pixel size of x/r, can be reconstructed from the multi-temporal set. The algorithm was applied to data from NASA's clas

关键词： Fusion super-resolution hyperspectral image imaging spectroscopy data HyspIRI AVIRIS

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Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data 8

Peculiarities of use of ECOC and AdaBoost based classifiers ...

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17th SPIE conference on Earth Resources and Environmental Remote Sensing/GIS Applications

作者： Dementyev, A. O. Dmitriev, E. V. Kozoderov, V. V. Egorov, V. D. Russian Acad Sci Inst Numer Math Ul Gubkina 8 Moscow 119333 Russia Moscow Inst Phys & Technol Inst Skiy Per 9 Dolgoprudnyi 141700 Moscow Region Russia Moscow MV Lomonosov State Univ Leninskiye Gory 1 Moscow 119991 Russia

ISBN: (纸本)9781510613218;9781510613201

hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

关键词： hyperspectral imagery pattern recognition classification of the forest vegetation forest inventory parameters

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Biased Normalized Cuts for Target Detection in hyperspectral imagery 22

Biased Normalized Cuts for Target Detection in Hyperspectral...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Zhang, Xuewen Dorado-Munoz, Leidy P. Messinger, David W. Cahill, Nathan D. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Image Comp & Anal Lab Rochester NY 14623 USA

ISBN: (纸本)9781510600812

The Biased Normalized Cuts (BNC) algorithm is a useful technique for detecting targets or objects in RGB imagery. In this paper, we propose modifying BNC for the purpose of target detection in hyperspectral imagery. As opposed to other target detection algorithms that typically encode target information prior to dimensionality reduction, our proposed algorithm encodes target information after dimensionality reduction, enabling a user to detect different targets in interactive mode. To assess the proposed BNC algorithm, we utilize hyperspectral imagery (HSI) from the SHARE 2012 data campaign, and we explore the relationship between the number and the position of expert-provided target labels and the precision/recall of the remaining targets in the scene.

关键词： Biased Normalized Cuts Dimensionality Reduction Laplacian Eigenmaps Schroedinger Eigenmaps Target Detection

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Anomaly detection in hyperspectral imagery: statistics- vs. graph-based algorithms 22

Anomaly detection in hyperspectral imagery: statistics- vs. ...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Berkson, Emily E. Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510600812

Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution;however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

关键词： anomaly detection topological anomaly detector (TAD) Reed-Xiaoli (RX) subspace-RX (SSRX) statistical graph-based scene clutter

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A nonlinear modeling framework for the detection of underwater objects in hyperspectral imagery 22

A nonlinear modeling framework for the detection of underwat...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Gillis, David B. Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9781510600812

The detection of underwater objects of interest (or targets) in hyperspectral imagery is a challenging problem, with a number of complications that are not present in land-based hyperspectral target detection. The main challenge in underwater detection is that, in contrast to land, where the observed spectrum of an associated target is largely independent of the surrounding background (e.g. the signature of a tank looks more or less the same whether it is on a road or in a field of grass), the observed spectrum of an underwater target is a highly nonlinear function of the background that is, the optical properties of the water body that the object is submerged in, as well as the depth of the target. As a result, the same object in different types of water and/or at different depths will in general have very different observed spectral signatures. In this work, we present a general overview of the various challenges involved in underwater detection, and present a novel approach that fuses forward radiative-transfer modeling, ocean color predictions, and nonlinear mathematical techniques (manifold learning) to model both the background and target signature(s) and perform detection over a wide range of environmental conditions and depths.

关键词： hyperspectral underwater target detection manifold learning

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Toward prediction of hyperspectral target detection performance after lossy image compression 22

Toward prediction of hyperspectral target detection performa...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Kaufman, Jason R. Vongsy, Karmon M. Dill, Jeffrey C. Univ Dayton Res Inst 300 Coll Pk Dr Dayton OH 45469 USA Air Force Res Lab 2241 Avion Circle Wright Patterson AFB OH 45433 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

ISBN: (纸本)9781510600812

hyperspectral imagery (HSI) offers numerous advantages over traditional sensing modalities with its high spectral content that allows for classification, anomaly detection, target discrimination, and change detection. However, this imaging modality produces a huge amount of data, which requires transmission, processing, and storage resources;hyperspectral compression is a viable solution to these challenges. It is well known that lossy compression of hyperspectral imagery can impact hyperspectral target detection. Here we examine lossy compressed hyperspectral imagery from data-centric and target-centric perspectives. The compression ratio (CR), root mean square error (RMSE), the signal to noise ratio (SNR), and the correlation coefficient are computed directly from the imagery and provide insight to how the imagery has been affected by the lossy compression process. With targets present in the imagery, we perform target detection with the spectral angle mapper (SAM) and adaptive coherence estimator (ACE) and evaluate the change in target detection performance by examining receiver operating characteristic (ROC) curves and the target signal-to-clutter ratio (SCR). Finally, we observe relationships between the data- and target-centric metrics for selected visible/near-infrared to shortwave infrared (VNIR/SWIR) HSI data, targets, and backgrounds that motivate potential prediction of change in target detection performance as a function of compression ratio.

关键词： hyperspectral imagery lossy image compression lossless image compression target detection performance performance evaluation

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Methods and challenges for target detection and material identification for longwave infrared hyperspectral imagery 22

Methods and challenges for target detection and material ide...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Rankin, Blake M. Meola, Joseph Perry, David L. Kaufman, Jason R. CNR Washington DC 20001 USA US Air Force Res Lab Washington DC 20330 USA Leidos Corp Reston VA USA Univ Dayton Res Inst Dayton OH 45469 USA

ISBN: (纸本)9781510600812

hyperspectral imaging (HSI) combined with target detection and identification algorithms require spectral signatures for target materials of interest. The longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, and thus, estimates of target temperature are necessary for emissivity retrieval through temperature-emissivity separation or for conversion of known emissivity signatures to radiance units. Therefore, lack of accurate target temperature information poses a significant challenge for target detection and identification algorithms. Previous studies have demonstrated both LWIR target detection using signature subspaces and visible/shortwave subpixel target identification. This work compares adaptive coherence estimator (ACE) and subspace target detection algorithms for various target materials, atmospheric compensation algorithms, and imagery domains (radiance or emissivity) for several data sets. Preliminary results suggest that target detection in the radiance and emissivity domains is complementary, in the sense that certain material classes may be more easily detected using subspaces, while others require conversion to emissivity space. Furthermore, a radiance domain LWIR material identification algorithm that accounts for target temperature uncertainty is presented. The latter algorithm is shown to effectively distinguish between materials with a high degree of spectral similarity.

关键词： longwave infrared target detection material identification radiance and emissivity domains

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Comparison of algorithms for blood stain detection applied to forensic hyperspectral imagery 22

Comparison of algorithms for blood stain detection applied t...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Yang, Jie Messinger, David W. Mathew, Jobin J. Dube, Roger R. Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510600812

Blood stains are among the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Early detection of blood stains is particularly important since the blood reacts physically and chemically with air and materials over time. Accurate identification of blood remnants, including regions that might have been intentionally cleaned, is an important aspect of forensic investigation. hyperspectral imaging might be a potential method to detect blood stains because it is non-contact and provides substantial spectral information that can be used to identify regions in a scene with trace amounts of blood. The potential material complexity of scenes in which such vast violence occurs can be high when the range of scene material types and conditions containing blood stains at a crime scene are considered. Some stains are hard to detect by the unaided eye, especially if a conscious effort to clean the scene has occurred (we refer to these as "latent" blood stains). In this paper we present the initial results of a study of the use of hyperspectral imaging algorithms for blood detection in complex scenes. We describe a hyperspectral imaging system which generates images covering 400 nm- 700 nm visible range with a spectral resolution of 10 nm. Three image sets of 31 wavelength bands were generated using this camera for a simulated indoor crime scene in which blood stains were placed on a T-shirt and walls. To detect blood stains in the scene, Principal Component Analysis (PCA), SubSpace Reed Xiaoli (SSRX), and Topological Anomaly Detection (TAD) algorithms were used. Comparison of the three hyperspectral image analysis techniques shows that TAD is most suitable for detecting blood stains in this environment and discovering latent blood stains.

关键词： Blood stain detection hyperspectral imagery crime scene PCA SSRX TAD

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