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

共 408 条记录，以下是61-70 订阅

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Landing Zone Identification for Autonomous UAV Applications Using Fused hyperspectral imagery and LIDAR Point Clouds 24

Landing Zone Identification for Autonomous UAV Applications ...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Lane, Sarah Kira, Zsolt James, Ryan Carr, Domenic Tuell, Grady Georgia Tech Res Inst Electroopt Syst Lab 925 Dalney St Atlanta GA 30332 USA Georgia Tech Res Inst Aerosp Transportat & Adv Syst Lab 250 14th St Atlanta GA 30332 USA 3D Ideas LLC 651 North Main St Madison GA 30650 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Multi-modal data fusion for situational awareness is of interest because fusion of data can provide more information than the individual modalities alone. However, many questions remain, including what data is beneficial, what algorithms work the best or are fastest, and where in the processing pipeline should data be fused? In this paper, we explore some of these questions through a processing pipeline designed for multi-modal data fusion in an autonomous UAV landing scenario. In this paper, we assess landing zone identification methods using two data modalities: hyperspectral imagery and LIDAR point clouds. Using hyperspectral image and LIDAR data from two datasets of Maui and a university campus, we assess the accuracies of different landing zone identification methods, compare rule-based and machine learning based classifications, and show that depending on the dataset, fusion does not always increase performance. However, we show that machine learning methods can be used to ascertain the usefulness of individual modalities and their resulting attributes when used to perform classification.

关键词： multi-modal data fusion hyperspectral imagery LIDAR autonomous UAV

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Image Registration and Change Detection for Artifact Detection in Remote Sensing imagery 24

Image Registration and Change Detection for Artifact Detecti...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Zelinski, Michael E. Henderson, John R. Held, Elizabeth L. Lawrence Livermore Natl Lab 7000 East Ave Livermore CA 94550 USA Oak Ridge Natl Lab 1 Bethel Valley Rd Oak Ridge TN 37830 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Image registration is used by the remote sensing community to align images for the purposes of examining changes in a scene. The application in this paper involves finding anomalies associated with human activity for the purpose of detecting underground nuclear explosions. This paper presents a non-rigid image registration algorithm that can be easily implemented using publicly available tools such as python, numpy, scipy, openCV and SIFT. SIFT is used to find feature correspondences between images. An approach based on Mahalanobis distance is used find a subset of robust correspondences. Comparisons are made to the RANSAC algorithm. The imagery was collected by DigitalGlobe's Worldview-ii satellite. One image pair is orthorectified. A second image pair is only geo-registered. Both image pairs were collected over mountainous desert regions, the second image pair has much rougher terrain and presents a challenging situation. The non-rigid property of the image registration algorithm allows for robust registration in mountainous terrain under different viewing geometries. Image differencing of the PAN-chromatic band is used to find changes, some of which are shown in detail for both sets of images. Overall registration improvement is quantified by using the standard deviation of the difference image. The non-rigid warping map was also applied to the multispectral bands of the DigitalGlobe data. This dataset made use of a multivariate change detection algorithm that incorporates the spectral properties of each pixel.

关键词： non-rigid image registration change detection multispectral remote sensing

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Multi-spectral Change Detection Methods: Evaluation on Simulated and Real-world Satellite imagery 24

Multi-spectral Change Detection Methods: Evaluation on Simul...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Mathew, Jobin J. Kerekes, John P. Rochester Inst Technol Digital Imaging & Remote Sensing Lab Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

This paper presents an evaluation of methods for detecting temporal changes in multi-spectral satellite imagery. A comparison of algorithm performance on both real and simulated satellite imagery is included. The real-world dataset (WorldView-2) required accurate registration and truth labeling, while the simulated satellite imagery and respective truth were readily available. An array of algorithms from conventional to statistical, distribution based, geometrical, graph-based and other approaches were evaluated. The Receiver-Operating-Characteristic (ROC) curve and Area-Under-ROC Curve (AUC) metrics were used for algorithm evaluation. Finally, the performance of various algorithms on simulated vs real images is discussed, which may be helpful in further applications of simulated imagery. The Graph-based methods, Chronochrome, Complexity and Outlier shift approaches performed well on both real and simulated datasets. A Python-based graphical user interface was written to automate the algorithm evaluation process and display the results.

关键词： Change Detection DIRSIG WorldView-2 ROC AUC Python

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Atmospheric correction of commercial thermal infrared hyperspectral imagery using FLAASH-IR 24

Atmospheric correction of commercial thermal infrared hypers...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Adler-Golden, Steven Guler, Nevzat Perkins, Timothy Spectral Sci Inc Burlington MA 01803 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Within the last few years, several commercial long-wave infrared (LWIR) hyperspectral imaging (HSI) systems have been developed for remote sensing of the ground from aircraft. While much less expensive and more practical to operate than sensors such as SEBASS and MAKO, which have been developed primarily for research and Government use, the commercial systems have poorer signal-to-noise and/or spectral resolution. We investigate the utility of three commercial systems-the Telops Hyper-Cam, SPECIM AisaOWL, and ITRES TASI-600-for quantitative retrieval of surface temperature and emissivity spectra. Atmospheric retrieval, correction and temperature-emissivity separation are performed on example data from these sensors using FLAASH-IR, a first-principles algorithm that incorporates radiation transport calculations and atmosphere models from MODTRAN. The results from the commercial sensors are noisy compared with SEBASS but otherwise appear to be reasonable. Applying a noise suppression algorithm to the radiance data yields better temperature retrievals and much cleaner emissivity spectra, with minimal loss of information, and should benefit scene classification applications.

关键词： LWIR hyperspectral noise suppression atmospheric correction Hyper-Cam AisaOWL TASI-600

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Supervised and unsupervised classification for obtaining land use/cover classes from hyperspectral and multi-spectral imagery 6

Supervised and unsupervised classification for obtaining lan...

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6th International conference on Remote Sensing and Geoinformation of the Environment (RSCy)

作者： Boori, M. S. Paringer, R. Choudhary, K. Kupriyanov, A. Samara Natl Res Univ 34 Moskovskoye Shosse Samara 443086 Russia Amer Sentinel Univ 2260 South Xanadu WaySuite 310 Aurora CO 80014 USA RAS Image Proc Syst Inst Molodogvardejskaya St 151 Samara Russia

ISBN: (纸本)9781510621183

In this study we compare supervised and unsupervised classification for land use/cover classes from hyperspectral and multispectral imagery. The algorithms include migrating means clustering (MMC) and k-nearest neighbor algorithm (KNN). We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Validation of the derived land-use/cover maps from the above two algorithms was performed through error matrix statistics using the validation points from the very high resolution imagery. Results show that both classification have high accuracy and useful for land use/cover classification but supervised classification slightly outperforming than unsupervised classification by overall higher classification accuracy and kappa statistics. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes' separation.

关键词： hyperspectral multispectral Satellite data land cover classification remote sensing Supervised and unsupervised classification

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Optimal Sensor Control for Fast Target Detection in hyperspectral imagery 24

Optimal Sensor Control for Fast Target Detection in Hyperspe...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Raz, Gil Chilenski, Mark Murphy, Cara Georgan, Chelsea Syst & Technol Res 600 West Cummings PkSuite 6500 Woburn MA 01801 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Real-time, standoff detection of trace chemicals on surfaces in the presence of unknown interferent contaminants using active IR spectroscopy poses significant challenges. The measurement time and computational burden can be prohibitive due to the number of spatial pixels, hundreds of potential wavenumbers, and size of the chemical library containing thousands of signatures. Therefore it is advantageous to optimally sample a small subset of the possible wavenumbers, where optimality is meant in the sense of detection and classification performance. Our approach accomplishes this by selecting wavenumbers which maximize the information about chemical identity. This is done by using submodular optimization, a technique which guarantees near-optimality at vanishingly low computational burden. Therefore, the methods shown here lend themselves to the time and resource constrained problems of data acquisition. This is in contrast to more traditional dimensionality reduction approaches such as lowering spectral resolution, random sparse sampling, and principal component analysis which degrade detection performance. In this work we describe methods for optimal illumination wavenumber selection to address the time constraints while addressing the challenges imposed by hardware and environmental artifacts (e.g., atmospheric effects).

关键词： hyperspectral imaging target detection optimal control information theory submodular optimization

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Low-dimensional enhanced superpixel representation with homogeneity testing for unmixing of hyperspectral imagery 24

Low-dimensional enhanced superpixel representation with homo...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Yi, Jiarui Velez-Reyes, Miguel Univ Texas El Paso Dept Elect & Comp Engn Sensor & Signal Analyt Lab 500 W Univ Ave El Paso TX 79968 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Recently superpixel-based approaches have been proposed for dimensionality reduction (DR) in hyperspectral images. The basic assumption of this approach is that the superpixel over-segmentation segments the image into small homogeneous areas. A low-dimensional (LD) image representation is obtained by using the average of the superpixels, which are then used in other image processing tasks up the processing chain. Due to superpixel-segmentation algorithm limitations, the region inside a superpixel may not be homogeneous. Therefore, the average may not be an adequate representation for the superpixel, leading to inaccuracies in the low dimensional representation resulting in errors in the image processing tasks or analysis. Here we present an enhanced superpixel-based dimensionality reduction approach that incorporates homogeneity testing of superpixels. Homogeneous superpixels are represented by their mean but heterogeneous superpixels are represented by multiple representative signatures selected using the SVDSS column subset selection algorithm. The representative signatures for the homogeneous and heterogeneous superpixels provide an improved low-dimensional representation for the hyperspectral image that better captures the image structure. We present experiments applying the proposed enhanced and the conventional superpixel dimensionality reduction approaches to unmixing using the constrained non-negative matrix factorization (cNMF). A subset of data from the Washington DC Mall HYDICE image is utilized. In the experiments, the enhanced superpixel-based dimensionality-reduction approach results in better unmixing results compared to the conventional approach and to unmixing using the full data set.

关键词： hyperspectral unmixing superpixel representation dimensionality reduction

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Enhanced target detection and identification using multispectral and hyperspectral polarimetric thermal measurements 15

Enhanced target detection and identification using multispec...

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conference on Electro-Optical and Infrared Systems - Technology and Applications XV

作者： Lavigne, Daniel A. Theriault, Jean-Marc Dion, Denis Fortin, Gilles Breton, Melanie Def Res & Dev Canada 2459 Bravoure Rd Quebec City PQ Canada Aerex Avion Inc 3510 St Augustin Levis PQ Canada

ISBN: (数字)9781510621749

ISBN: (纸本)9781510621749

The performance of thermal electro-optic infrared sensors (EO/IR) may be limited in certain specific circumstances, particularly for the detection and identification of targets embedded in an isothermal scene, i.e. when there is insufficient thermal contrast between the targets against their surrounding background. Such situations generally occur at the beginning and end of the day, but can also happen at any time during the day. One way to cope with this limitation is to employ EO/IR sensors that are sensitive to the polarization states of light. With this intention, Defence Research and Development Canada (DRDC) has developed thermal infrared multispectral and hyperspectral polarimetric imaging systems and spectral algorithms to extract the polarized radiance components of targets of interest, and use this additional information to enhance detection and identification while reducing false alarm rate. This paper presents experimental results from measurements using ground-based multispectral and hyperspectral polarimetric imaging sensors to acquire the polarized radiance of targets set up at multiple orientation angles with respect to the sensors line-of-sight (LOS). The objectives of the experiments were to study the phenomenology of polarized surface radiance in the Long-Wave Infrared (LWIR) and assess the effect of different materials on the resulting s-polarized and p-polarized spectral components. Experimental results show the advantages of thermal multispectral and hyperspectral polarimetric imaging sensors over conventional unpolarized ones to discriminate targets against their background, particularly during thermal cross-over periods.

关键词： Polarimetric imagery hyperspectral imagery thermal infrared spectral polarization

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Sensitivity of Temperature and Emissivity Separation to Atmospheric Errors in LWIR hyperspectral imagery 24

Sensitivity of Temperature and Emissivity Separation to Atmo...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Pieper, M. L. Manolakis, D. Truslow, E. Jacobson, J. Weisner, A. Cooley, T. Ingle, V. MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA Natl Air & Space Intelligence Ctr Wright Patterson AFB OH 45433 USA Air Force Res Lab Space Vehicles Directorate 2251 Maxwell Ave Kirtland AFB NM 87117 USA Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Accurate estimation of surface emissivity from long-wave infrared (LWIR) hyperspectral imaging data acquired by airborne or space-borne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation (AC) and temperature-emissivity separation (TES). AC uses an estimated atmospheric model to estimate the ground radiance from the at-aperture radiance, and then TES produces a temperature and emissivity estimate using this ground radiance. hyperspectral TES algorithms assume that the emissivity spectra for solids are smooth compared to the atmosphere. The quality of TES estimates depend on the accuracy of the atmospheric estimates and their band-averaging to the sensor spectral resolution. The objective of this paper is to study the sensitivity of TES results to errors in atmospheric estimates. There are several errors associated with TES output including temperature errors, and shape, bias, and smoothness errors in the emissivity. The effects of atmospheric errors are studied by using MODTRAN (R) to generate models with varying temperature and water vapor amounts. One model is used to simulate at-aperture radiances while the others are used for AC and TES on the simulated spectra. The mismatch between the models affects the TES results. While most errors cause TES to fail providing rough emissivity estimates, magnitude and shape errors provide smooth but incorrect emissivities. Understanding how atmospheric errors affect the TES estimates and what is required of atmospheric estimates to provide smooth emissivity estimates will help in developing algorithms to find the correct atmosphere for a scene.

关键词： hyperspectral imaging target detection CFAR processing matched filtering

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multispectral and hyperspectral Image Fusion by MS/HS Fusion Net

Multispectral and Hyperspectral Image Fusion by MS/HS Fusion...

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IEEE/CVF conference on Computer Vision and Pattern Recognition

作者： Qi Xie Minghao Zhou Qian Zhao Deyu Meng Wangmeng Zuo Zongben Xu Xi'an Jiaotong University Harbin Institute of Technology

ISBN: (纸本)9781728132945

hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS) images can generally be captured at video rate in practice. In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image. In specific, we construct a novel MS/HS fusion model which takes the observation models of low-resolution images and the low-rankness knowledge along the spectral mode of HrHS image into consideration. Then we design an iterative algorithm to solve the model by exploiting the proximal gradient method. And then, by unfolding the designed algorithm, we construct a deep network, called MS/HS Fusion Net, with learning the proximal operators and model parameters by convolutional neural networks. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state-of-the-art methods along this line of research.

关键词： Fusion reactions Fusion, biological FUSION fusion (melting) hyperspectral imagery multispectral images Iterative algorithms image systems observational data Nets model parameters simulating Low-resolution image

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