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algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXV 2019

ISBN: (纸本)9781510626379

The proceedings contain 57 papers. The topics discussed include: unsupervised hyperspectral band selection in the compressive sensing domain;simplified measurement of point spread functions of hyperspectral cameras for assessment of spatial coregistration;solar-induced fluorescence retrievals in the context of physiological, environmental, and hardware-based sources of uncertainty;stray light characterization in a high-resolution imaging spectrometer designed for solar-induced fluorescence;SAGE iv pathfinder multi-spectral imaging spectrometer telescope paves the way for semi-custom CubeSat imaging missions;extended characterization of multispectral resolving filter-on-chip snapshot-mosaic CMOS cameras;advances in active infrared spectroscopy for trace chemical detection;and active LWIR hyperspectral imaging and algorithms for rapid standoff trace chemical identification.

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Very High-Resolution imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types

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REMOTE SENSING 2022年第4期14卷 954页

作者： Rommel, Edvinas Giese, Laura Fricke, Katharina Kathoefer, Frederik Heuner, Maike Moelter, Tina Deffert, Paul Asgari, Maryam Naethe, Paul Dzunic, Filip Rock, Gilles Bongartz, Jens Burkart, Andreas Quick, Ina Schroeder, Uwe Baschek, Bjoern German Fed Inst Hydrol Landscape Management Dept Vegetat Studies Am Mainzer Tor 1 D-56068 Koblenz Germany German Fed Inst Hydrol Dept Geodesy & Remote Sensing Am Mainzer Tor 1 D-56068 Koblenz Germany German Fed Inst Hydrol Dept Fluvial Morphol Sediment Dynam & Management Am Mainzer Tor 1 D-56068 Koblenz Germany GEOCOPTIX GmbH Max Planck Str 12 D-54296 Trier Germany Univ Appl Sci Koblenz Dept Math & Technol Joseph Rovan Allee 2 D-53424 Remagen Germany JB Hyperspectral Devices GmbH Am Botanischen Garten 33 D-40225 Dusseldorf Germany

Riparian zones fulfill diverse ecological and economic functions. Sustainable management requires detailed spatial information about vegetation and hydromorphological properties. In this study, we propose a machine learning classification workflow to map classes of the thematic levels Basic surface types (BA), Vegetation units (VE), Dominant stands (DO) and Substrate types (SU) based on multispectral imagery from an unmanned aerial system (UAS). A case study was carried out in Emmericher Ward on the river Rhine, Germany. The results showed that: (I) In terms of overall accuracy, classification results decreased with increasing detail of classes from BA (88.9%) and VE (88.4%) to DO (74.8%) or SU (62%), respectively. (II) The use of Support Vector Machines and Extreme Gradient Boost algorithms did not increase classification performance in comparison to Random Forest. (III) Based on probability maps, classification performance was lower in areas of shaded vegetation and in the transition zones. (iv) In order to cover larger areas, a gyrocopter can be used applying the same workflow and achieving comparable results as by UAS for thematic levels BA, VE and homogeneous classes covering larger areas. The generated classification maps are a valuable tool for ecologically integrated water management.

关键词： UAS airborne imagery remote sensing classification random forest OBIA management riparian zones hydromorphology

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SAGE iv Pathfinder multi-spectral imaging spectrometer telescope paves the way for semi-custom CubeSat imaging missions 25

SAGE IV Pathfinder multi-spectral imaging spectrometer teles...

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25th SPIE Conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Halterman, Alexander Cheff Damadeo, Robert Hill, Charles Buleri, Christine Murchison, Luke Obland, Michael Phenis, Adam Yacoby, Shimshone Quartus Engn Inc 9689 Towne Ctr Dr San Diego CA 92121 USA NASA Langley Res Ctr LARC 1 NASA Dr Hampton VA 23666 USA AMP Opt LLC 13308 Midland RdUnit 1304 Poway CA 92074 USA

ISBN: (纸本)9781510626386

The SAGE iv (Stratospheric Aerosol and Gas Experiment) Pathfinder looks towards ushering in the next generation of the SAGE family of instruments, leveraging solar occultation to retrieve vertical profiles of aerosols and gases in the stratosphere, providing high precision calibration data for other instruments. A development funded through the NASA Earth Science Technology Office (ESTO) Instrument Incubator Program ( IIP) SAGE iv Pathfinder is designed to extend the data record from the SAGE III scanning grating spectrometer with a multispectral imaging approach. Solar disk imaging improves the data collected by providing: (1) absolute pointing information;(2) measurements of atmospheric refraction effects;and (3) measurements of solar disk anisotropy. This additional information relaxes traditionally tight constraints on attitude knowledge, stability, and pointing control making a free-flying 6U CubeSat instrument feasible. Early estimates show this approach might reduce the cost of SAGE continuity missions by as much as 90%. A key benefit of the SAGE iv Pathfinder design to future missions is the versatility of the resultant telescope subsystem. The F/5.25 telescope resulted in >90% encircled energy within a 30 mu m/28 arcsecond pixel and point source normalized irradiance transmittance (PSNIT) of <1E-4 0.5 degrees outside of the field of view (FOV). The baseline design can be adapted to accommodate changes to layout, aperture, focal lengths, filters, and/or detectors in various CubeSat form factors. The telescope was designed to be thermally agnostic, with STOP analysis results indicating negligible performance variation as thermal gradients fluctuate on orbit. Once thermal validation of STOP analysis is completed, proven micron-level alignment, mounting, and analyses can then be leveraged for new high performance, semi-custom instruments, saving significant development cost for future science missions.

关键词： SAGE CubeSat telescope imaging spectrometer NASA three-mirror anastigmat STOP analysis

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Simultaneous Spectral/Spatial Detection of Edges for hyperspectral imagery: The HySPADE Algorithm Revisited

Simultaneous Spectral/Spatial Detection of Edges for Hypersp...

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Annual Conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVIII

作者： Resmini, Ronald G. George Mason Univ Dept Geog & GeoInformat Sci Fairfax VA 22030 USA

ISBN: (纸本)9780819490681

The hyperspectral/spatial detection of edges (HySPADE) algorithm, originally published in 2004 [1], has been modified and applied to a wider diversity of hyperspectral imagery (HSI) data. As originally described in [1], HySPADE operates by converting the naturally two-dimensional edge detection process based on traditional image analysis methods into a series of one-dimensional edge detections based on spectral angle. The HySPADE algorithm: i) utilizes spectral signature information to identify edges;ii) requires only the spectral information of the HSI scene data and does not require a spectral library nor spectral matching against a library;iii) facilitates simultaneous use of all spectral information;iv) does not require endmember or training data selection;v) generates multiple, independent data points for statistical analysis of detected edges;vi) is robust in the presence of noise;and vii) may be applied to radiance, reflectance, and emissivity data-though it is applied to radiance and reflectance spectra (and their principal components transformation) in this report. HySPADE has recently been modified to use Euclidean distance values as an alternative to spectral angle. It has also been modified to use an N x N-pixel sliding window in contrast to the 2004 version which operated only on spatial subset image chips. HySPADE results are compared to those obtained using traditional (Roberts and Sobel) edge-detection methods. Spectral angle and Euclidean distance HySPADE results are superior to those obtained using the traditional edge detection methods;the best results are obtained by applying HySPADE to the first few, information-containing bands of principal components transformed data (both radiance and reflectance). However, in practice, both the Euclidean distance and spectral angle versions of HySPADE should be applied and their results compared. HySPADE results are shown;extensions of the HySPADE concept are discussed as are applications for HySPADE in HSI

关键词： edge detection hyperspectral imagery HSI spatial spectral spectral angle Euclidean distance Roberts Sobel

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McIDAS-V - A powerful data analysis and visualization tool for multi and hyperspectral environmental satellite data

McIDAS-V - A powerful data analysis and visualization tool f...

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Conference on Atmospheric and Environmental Remote Sensing Data Processing and Utilization iv - Readiness for GEOSS II

作者： Achtor, Thomas Rink, Thomas Whittaker, Thomas Parker, David Santek, David Univ Wisconsin Ctr Space Sci & Engn Madison WI 53706 USA

ISBN: (纸本)9780819473059

The Mail computer Interactive Data Access System (McIDAS) project began over 30 years ago at the University of Wisconsin-Madison to analyze and visualize data from the first generation of geostationary weather satellites. McIDAS continues to provide a strong data analysis and visualization capability for the current environmental satellites. However, the next generation of operational remote sensing instruments under development for the NPOESS and GOES-R programs require software tools with expanded capability and performance to support innovative techniques for developing algorithms, visualizing data and products, and evaluating results. A project is underway at SSEC to develop the fifth generation of McIDAS, a java-based, open-source system for multispectral and hyperspectral researchers and algorithm developers that will provide powerful new data manipulation and visualization tools to work in this data rich environment. NASA EOS MODIS and AIRS data as well as MSG SEVERI and METOP IASI data are now being used in conjunction with in situ and gridded data to develop new analysis and product validation techniques in the McIDAS-V environment. This new data analysis and visualization system will support both researchers and operational users of the advanced measurement systems on NPOESS and GOES R.

关键词： McIDAS visualization data analysis satellite imagery GOES NPOESS

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algorithms for multispectral and hyperspectral imagery iv

Algorithms for Multispectral and Hyperspectral Imagery IV

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1998年

作者： Sylvia S Shen Michael R Descour

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Proceedings of SPIE: algorithms for multispectral and hyperspectral imagery iv

Proceedings of SPIE: Algorithms for Multispectral and Hypers...

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algorithms for multispectral and hyperspectral imagery iv

This Volume 3372 of the conference proceedings contains 22 papers. Topics discussed include algorithms for multispectral and hyperspectral imagery, detection and classification, image enhancement and data compression,... 详细信息

关键词： Imaging techniques

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Wavelet-based noise reduction in multispectral imagery

Wavelet-based noise reduction in multispectral imagery

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Conference on algorithms for multispectral and hyperspectral imagery iv

作者： Basuhail, AA Kozaitis, SP Florida Inst Technol Div Elect & Comp Sci & Engn Melbourne FL 32901 USA

ISBN: (纸本)0819428213

We used a 3-D wavelet-based denoising method to reduce the noise from multispectral imagery. In our approach, we compared denoising of different bands of a multispectral image using a 2-D denoising technique, by which the wavelet coefficients corresponding to each band were denoised independent of each band, and a 3-D denoising technique by which the wavelet coefficients were denoised by involving all bands in thresholding the wavelet coefficients. Due to the high correlation of the multispectral imagery data along the wavelength axis, the noise can be easily reduced by applying the wavelet transform along the wavelength direction. Our results showed that the 3-D denoising approach improved the overall SMI of a noisy multispectral imagery over the 2-D denoising approach, due to the correlation between the different bands.

关键词： denoising three-dimensional wavelet transform multispectral

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Detection in hyperspectral imagery

Detection in hyperspectral imagery

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Conference on algorithms for multispectral and hyperspectral imagery iv

作者： Schweizer, SM Moura, JMF Carnegie Mellon Univ ECE Dept Pittsburgh PA 15213 USA

ISBN: (纸本)0819428213

hyperspectral sensors collect hundreds of images in contiguous and narrowly spaced spectral bands. They have the potential to simultaneously provide high spatial and spectral resolution of targets of interest in Automatic Target Detection and Recognition (ATD/R). The price to be paid is the need to process and store an extremely large amount of data in an effective and timely manner. We develop a new implementation of the maximum-likelihood (ML) detector which is both practical and efficient. Our detection is based on a Gauss-Markov Random Field (GMRF) model for the data which avoids the inversion of large data covariance matrices usually encountered in ML-detectors. The paper presents two algorithms to fit the GMRF to the hyperspectral sensor data: an optimal ML estimation algorithm and a suboptimal Least Squares (LS) estimation algorithm. Using the LS-algorithm, we develop the structure of the detector and present estimation results from a real hyperspectral data set.

关键词： hyperspectral Gauss-Markov random field maximum-likelihood detector least squares estimation

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Robust spatial and spectral feature extraction for multispectral and hyperspectral imagery

Robust spatial and spectral feature extraction for multispec...

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Conference on algorithms for multispectral and hyperspectral imagery iv

作者： Pinzon, JE Ustin, SL Castaneda, CM Pierce, JF Costick, LA Univ Calif Davis Dept Math Davis CA 95616 USA

ISBN: (纸本)0819428213

We present a hierarchical classification technique that discriminates broad categories of surface materials in terms of ground true features, such as water, vegetation, and soils from spectral information. Subsequently, we further discriminate these materials and extract finer ground features, like chemistries, peculiar to each. The interaction at various scales of the 3D spatial and the spectral domains is decomposed by using wavelet tools to address scale dependencies in the spatial domain, a robust spectral unmixing technique, called Hierarchical Foreground Background Analysis (HFBA) along the spectral axis. HFBA sequentially derives a series of weighting vectors discriminating features at different levels of detection: (1) constituent materials, (2) types within constituents, and (3) chemistries peculiar to each type. Our goal is two-fold. First, we present the combination of HFBA and wavelets as a supervised classification technique validating the categories imposed by the supervised classification, and manifesting clusters which can refine the classification at different scales. Second, we identify spectral redundancies between hyperspectral and multispectral information, studying mixtures at different spatial/spectral resolutions and assess whether targeted features may be extracted as efficiently from multispectral data as they could be from hyperspectral data. Results on AVIRIS and simulated MODIS data illustrate the robustness and effectivity of the technique.

关键词： supervised classification multispectral hyperspectral imagery wavelets non-linear mixture analysis

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