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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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Geo-spatial image and data exploitation ii

Geo-spatial image and data exploitation II

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Geo-Spatial Image and Data Exploitation ii

The proceedings contains 11 papers from SPIE conference on optical engineering . The topics discussed include: imagery spectroscopy;hyperspectral imaging in earth road construction planning;hyperspectral remote sensing data;image mosaics applied to vegetation identification;multispectral image fusion;remote sensing image registration;geo-spatial sources;hyperspectral data processing methods;hypersepctral image analysis and image data integration and analysis for natural disaster decision support systems.

关键词： Remote sensing

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Unified Lagrangian Neural Network method for subpixel classification in hyperspectral imagery

Unified Lagrangian Neural Network method for subpixel classi...

引用

8th Annual conference on Wavelet Applications

作者： Szu, H Ren, H Off Naval Res Arlington VA 22217 USA

ISBN: (纸本)0819440868

Because of the foot print of a pixel is relative large, one pixel usually contains more than one material. The spectrum of each pixel vector can be considered as spectral mixture of all materials present in that pixel. How to unmix the spectra while dealing with constraint at the same time is a challenging problem in spectral mixture analysis. One approach is supervised constrained least squares approach, which has full knowledge about the spectrum signatures of endmembers resident in the image scene, The other is unsupervised unmixing method using Lagrangian Artificial Neural Network (LANN) with no knowledge about the image scene. The concept of unit-sum constraint is identical in both cases, the difference is whether one uses the known spectral characteristics for the supervised training or not. In practice, we need both methodologies for efficiency reasons, since there is "curse of dimensionality" (too large degree of freedom in hyperspectral). The supervised one can reduce the search and ID size and the unsupervised one can discover the unknown interferences and thus in constrained least squares algorithms help ID. The constrained least squares method was been discussed in digital signal processing and applied to hyperspectral imagery while LANN to multispectral imagery. In this paper, we expend LANN to hyperspectral image classification and also discuss the relationship between the constrained least squares method and LANN. These two methods alleviate this problem by adopting the Lagrange multiplier in neural network to relax the sum-to-one constraint. To evaluate this designed algorithm a series of experiments using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images are conducted to show its potential usefulness in hyperspectral image classification.

关键词： hyperspectral imaging Neurons Image classification Independent component analysis Neural networks Earth observing sensors Landsat Remote sensing Vegetation multispectral imaging

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Estimation of vegetation parameters by inversion of radiative-transfer models from multispectral imagery supported by ground control measurements

Estimation of vegetation parameters by inversion of radiativ...

引用

conference on multispectral and hyperspectral Image Acquisition and Processing

作者： Kurz, F Hellwich, O GSF Natl Res Ctr Environm & Hlth D-85764 Munich Germany

ISBN: (纸本)0819442763

We propose a general framework to estimate vegetation parameters from multidimensional remote sensing data using physical models and a moderate amount of ground control data. This framework is exemplarily demonstrated for winter wheat fields imaged by the Daedalus multispectral scanner using physical radiative transfer models, like the SAIL and PROSPECT model, combined with a linear empirical model. Results show the invertibility of the model for leaf area index, chlorophyll content, dry matter, and water content. The attained accuracies of these parameters are compared to the amount of ground control data. The vegetation parameters estimated with help of the devised method are intended to be used to derive information about soil heterogeneities that are important for precision farming.

关键词： Vegetation Sensors Data modeling Reflectivity Scanners multispectral imaging Remote sensing Sun algorithms Radiative transfer

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Performance analysis of the adaptive GMRF anomaly detector for hyperspectral imagery

Performance analysis of the adaptive GMRF anomaly detector f...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Thornton, SS Moura, JMF Media Site Inc Pittsburgh PA USA

ISBN: (纸本)0819436755

We have developed an adaptive anomaly detector based on a three-dimensional Gauss-Markov random field model (GMRF) for the background clutter in hyperspectral imagery. We have shown that incorporating the modeling framework into a single-hypothesis detection scheme leads to an efficient and effective algorithm for discriminating manmade objects (the anomalies) in real hyperspectral imagery. A major feature of our GMRF anomaly detector is that it adapts to the local statistics of the clutter through the use of an approximate maximum-likelihead (approximate-ML) estimation scheme. In this paper, we focus on a thorough performance evaluation of our Adaptive GMRF Anomaly detector for hyperspectral imagery. We evaluate the detector along three directions: estimation error performance, computational cost, and detection performance. In terms of estimation error, we derive the Cramer-Rao bounds and carry out Monte Carlo simulation studies that show that the approximate-hll estimation procedure performs quite well when the fields are highly correlated, as often the case with real hyperspectral imagery. Finally we test extensively the adaptive anomaly detector with real hyperspectral imagery from both the HYDICE and SEBASS sensors. The performance of our anomaly detector compares very favorably with that of the RX-algorithm, an alternative maximum-likelihood detector used with multispectral data, while reducing by up to an order of magnitude the associated computational cost.

关键词： hyperspectral imagery anomaly detector Gauss-Markov random field approximate ML-estimation single hypothesis detection Cramer-Rao bound SEBASS HYDICE

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Simulation of MSI imagery from HSI data

Simulation of MSI imagery from HSI data

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Anderson, DS Keaffaber, B Wasky, R Veridian Engineering (United States)

ISBN: (纸本)0819436755

In this paper, a process of converting hyperspectral (HSI) data to multispectral (MSI) data is described. Accomplishing this conversion involves several steps: (1) data-to-signal conversion using the modulation transfer function (MTF) and the Spectral Response of the HSI sensor, (2) removing atmospheric transmittance losses from the HSI data using the MODTRAN code, (3) reintroducing atmospheric transmittance effects for each HSI band over selected MSI slant ranges, (4) grouping appropriate HSI bands into the broader MSI bands, (5) applying a spatial sampling algorithm to simulate different MSI ground sample distances (GSDs), and (6) performing signal-to-data conversion using the MTF and Spectral Response of the MSI sensor. Results are presented for two different slant ranges (GSD levels), each with three meteorological ranges.

关键词： Sensors multispectral imaging Modulation transfer functions Atmospheric corrections Image processing Transmittance Atmospheric modeling Atmospheric sensing Data conversion Image sensors

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Target detection enhancement using temporal signature propagation

Target detection enhancement using temporal signature propag...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Mayer, R Schaum, A SFA Inc Largo MD 20774 USA

ISBN: (纸本)0819436755

Changes in atmospheric conditions and sensor response for successive imaging sessions have limited the use of fixed target hyperspectral libraries to help discriminate targets from cluttered backgrounds. The hyperspectral target signature instability and unpredictability have resulted in a dependence on anomaly detection algorithms in real time surveillance applications. However, the performance of these techniques fails to meet system requirements for many military applications. Our study examines temporal variations in the long-wave infrared spectra of man-made targets and natural backgrounds obtained with the SEBASS (8-12 mu m) imager as part of the Dark HORSE 2 exercise during the HYDRA data collection in November, 1998. We examine the signature information propagated over various time intervals. In addition, the hyperspectral target signatures, taken over various time intervals, are transformed using image-based techniques and compared to those converted with methods based on atmospheric modeling. We show that detection performance can be dramatically improved by exploiting signature information propagated over various time intervals using image-based methods. Implications of this study for target detection enhancement using hyperspectral data derived from multiple flight missions and/or mathematical transformations of hyperspectral target signatures will be discussed.

关键词： automatic target detection algorithms for hyperspectral imagery remote sensing

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Atmospheric correction of spectral imagery data

Atmospheric correction of spectral imagery data

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Meidunas, E Acharya, PK Adler-Golden, SM Anderson, GP Armstrong, PS Berk, A Berstein, LS Felde, G Gardner, JA Hoke, M Matthew, MW Ratkowski, AJ USAF Res Lab Space Vehicles Directorate AFRLVSBM Hanscom AFB MA 01731 USA

ISBN: (纸本)0819436755

Atmospheric emission, scattering and photon absorption degrade spectral imagery data and reduce its utility. The Air Force Research Laboratory and Spectral Sciences, Inc, are developing a MODTRAN4-based 'atmospheric mitigation' algorithm to support current and planned IR-visible-UV sensor spectral radiance imagery measurements. The intent is to provide surface reflectance and emissivity imagery data of sufficient accuracy for input into subsequent analyses of sur;face properties, effectively removing the atmospheric component. This report is the result of the application of the atmospheric mitigation algorithm to a NASA/JPL AVIRIS spectral image cube as a pre-processing step towards improving the performance of image categorization routines.

关键词： Reflectivity Atmospheric corrections Visibility Image processing Analytical research Data corrections Atmospheric sensing Light scattering Roads Sensors

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Detection and segmentation in hyperspectral imagery using discriminant analysis

Detection and segmentation in hyperspectral imagery using di...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Meth, R ImageCorp Inc College Pk MD 20740 USA

ISBN: (纸本)0819436755

Hyper-spectral imagery (HSI) contains significant spectral resolution that enables material identification. Typical methods of classification include various forms of matching sample image spectra to pure end-member sample spectra or mixtures of these end-members. Often, pure end-members are not available a-priori. We propose the use of HSI to complement other sensor modalities which are used to cue the end-member selection process for target detection, Multiple sensor modalities are frequently available and sensor fusion is exploited as demonstrated by the DARPA Dynamic Database (DDB) and Multisensor Exploitation Testbed (MSET) programs. Candidate target pixels, cued from other sensor modalities, are registered to the HSI and verified using local matched filters. Target identification is then performed using multiple methods including Euclidean distance, spectral angle mapping, anomaly detection, principal component analysis (PCA) decomposition and reconstruction, and linear discriminant analysis (LDA). The use of LDA for target identification as well as scene segmentation provides significant capabilities to HSI understanding.

关键词： HSI target detection scene segmentation sensor fusion

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Comparison of matrix factorization algorithms for band selection in hyperspectral imagery

Comparison of matrix factorization algorithms for band selec...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Vélez-Reyes, M Jiménez, LO Linares, DM Velázquez, HT Univ Puerto Rico Dept Elect & Comp Engn Lab Applicat Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819436755

hyperspectral imaging sensors provide high-spectral resolution images about natural phenomena in hundreds of bands, High storage and transmission requirements, computational complexity, and statistical modeling problems motivate the idea of dimension reduction using band selection. The optimal band-selection problem can be formulated as a combinatorial optimization problem where p-bands from a set of n-bands are selected such that some measure of information content is maximized. Potential applications for automated band selection include classifier feature extraction, and band location in sensor design and in programming of reconfigurable sensors. The computational requirements for standard search algorithms to solve the optimal band selection problem are prohibitive. In this paper, we present the use of singular value and rank revealing QR matrix factorizations for band selection. These matrix factorizations can be use to determine the most independent columns of a matrix. The selected columns represent the most independent bands that contain most of the spatial information. It can be shown that under certain circumstances, the bands selected using these matrix factorizations are good approximations to the principal components explaining most of the image spatial variability. The advantage of matrix factorizations over the combinatorial optimization approach is that they take polynomial time and robust and proven numerical routines for their computation and are readily available from many sources. In the paper, we will present results comparing the performance of the algorithms using AVIRIS and LANDSAT imagery. algorithms are compared in their computational requirements, their capacity to approximate the principal components, and their performance as an automated feature extraction processor in a classification algorithm. Preliminary results show that under certain circumstances selected bands can have over 90% correlation with principal components and classifiers

关键词： hyperspectral imaging Earth observing sensors Landsat Sensors Dimension reduction Feature extraction Data modeling Image sensors Statistical analysis Image processing

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Comprehensive hyperspectral system simulation ii: hyperspectral sensor simulation and preliminary VNIR testing results

Comprehensive hyperspectral system simulation II: hyperspect...

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algorithms for multispectral, hyperspectral, and Ultraspectral imagery VI

作者： Bartell, Richard J. Schwartz, Craig R. Eismann, Michael T. Cederquist, John N. Nunez, James A. Sanders, Lee C. Ratcliff, Alan H. Lyons, Barry W. Ingle, Stephen D. Veridian ERIM Int Ann Arbor United States

An end-to-end hyperspectral system model with applications to space and airborne sensor platforms is under development and testing. In this paper we discuss current work in the development of the sensor model and the results of preliminary testing. It is capable of simulating collected hyperspectral imagery of the ground as sensors operating from space or airborne platforms would acquire it. Dispersive hyperspectral imaging sensors operating from the visible through the thermal infrared spectral regions can be modeled with actual hyperspectral imagery or simulated hyperspectral scenes used as inputs. In the sensor model portion, fore-optics (misalignment), dispersive spectrometer designs, degradations (platform motion, smile, keystone, misregistration), focal plane array (temperature drift, nonuniformity/nonlinearity), noise (shot, dark, Johnson, 1/f, RMS read, excess low frequency), analog-to-digital conversion, digital processing, and radiometric/temporal/wavelength calibration effects are included. The overall model includes a variety of processing algorithms including constant false alarm rate anomaly detection, spectral clustering of backgrounds for anomaly detection, atmospheric compensation, and pairwise adaptive linear matching for detection and classification. Results of preliminary testing using synthetic scene data in the visible/near infrared portion of the spectrum are discussed. Potential applications for this modeling capability include processing results performance prediction and sensor parameter specification trade studies.

关键词： Image sensors

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