Results 261 to 270 of about 35,627 (298)

Adaptive coding of hyperspectral imagery

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
Two systems are presented for compression of hyperspectral imagery. These systems utilize adaptive classification, trellis-coded quantization, and optimal rate allocation. In the first system, DPCM is used for spectral decorrelation, while an adaptive wavelet-based coding scheme is used for spatial decorrelation.
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Change Analysis for Hyperspectral Imagery

2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images, 2007
In this paper, the change vector analysis for hyperspectral imagery is investigated. Although plentiful change information is included in the change vectors with very high dimensionality, which permits the potential of finer change analysis, it is also very challenging to analyze these change vectors since any simple deterministic approaches using ...
Qian Du, Nicholas Younan, Roger King
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Pavement management using hyperspectral imagery

SPIE Proceedings, 2003
Public Works facilities require up-to-date information on the health status of the road network they maintain. However, roadway maintenance and rehabilitation involves the greatest portion of a municipality's annual operating budget. Government officials use various technologies such as a pavement management system to assist in making better decisions ...
Balehager Ayalew, Richard B. Gomez, William E. Roper, Oscar Carrasco   +3 more
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Band prioritization for hyperspectral imagery

SPIE Proceedings, 2006
Hyperspectral images are collected by hundreds of contiguous spectral channels and thus, the data volume to be processed is considered to be huge. With such high spectral resolution, spectral correlation among bands is expected to be very high. Band selection (BS) is one of common practices to reduce data volumes, while retaining desired information ...
Su Wang, Chein-I Chang
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Dimensionality reduction in hyperspectral imagery

SPIE Proceedings, 2003
In this paper we examine how the projection of hyperspectral data into smaller dimensional subspaces can effect the propagation of error. In particular, we show that the nonorthogonality of endmembers in the linear mixing model can cause small changes in band space (as, for example, from the addition of noise) to lead to relatively large changes in the
David Gillis, Jeffrey H. Bowles, Michael E. Winter   +2 more
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Interest segmentation of hyperspectral imagery

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010
In recent years, many new methods for analyzing spectral imagery have been introduced. These new methods have been developed to improve the analysis of hyperspectral imagery. Many of these techniques are data driven anomaly/target detection and spectral clustering algorithms which are used to decide whether a particular pixel or area is “interesting ...
Ariel Schlamm, David Messinger, William Basener   +2 more
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Anomaly Detection in Hyperspectral Imagery

2016
In this chapter we are presenting the literature and proposed approaches for anomaly detection in hyperspectral images. These approaches are grouped into four categories based on the underlying techniques used to achieve the detection: 1) the statistical based methods, 2) the kernel based methods, 3) the feature selection based methods and 4) the ...
Karim Saheb Ettabaa, Manel Ben Salem
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Coastal Characterization from Hyperspectral Imagery

Imaging and Applied Optics Congress, 2010
Coastal mapping products and models from hyperspectral remote sensing experiments in different coastal types are compared: barrier island coast (Virginia, 2007), coral coast (Hawaii 2009), mangrove coast (Australia, 2009), and coral limestone and volcanic coasts (Guam and CNMI, 2010). Article not available.
Charles M. Bachmann, C. Reid. Nichols, Marcos J. Montes, Robert A. Fusina, John C. Fry, Rong-Rong Li, Deric Gray, Daniel Korwan, Christopher Parrish, Jon Sellars, Stephen A. White, Jason Woolard, Krista Lee, Cecilia McConnon, Jon Wende   +14 more
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Lossless Compression of Hyperspectral Imagery

2011 First International Conference on Data Compression, Communications and Processing, 2011
In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances ...
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Multiscale, multivariate autoregressive detection for hyperspectral imagery

Proceedings of SPIE, 2009
There are often demands for region target detection such as air pollution detection and oil spills monitoring, even though small target detection has gained much attention in the field of hyper spectral detection. In this paper, we present a multiscale-multivariate autoregressive (MMA) method to handle such region targets, considering the spatial ...
Lin He, Wei Di, Zhuliang Yu, Yuanqing Li, Zhenghui Gu   +4 more
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