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Global, local, and stochastic background modeling for target detection in mixed pixels
2010
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
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Many common target detection algorithms, such as the Adaptive Coherence/Cosine Estimator, implicitly use a global background model that assumes the background can be modeled by a single, multivariate Gaussian ...
This paper introduces an improved variant of the local background model, as well as a novel stochastic background model that is free from distributional assumptions and accounts for the spectral variability ...
The author also would like to acknowledge the Rochester Institute of Technology for providing access to Target Detection Blind Test project. This technical data was produced for the U. S. ...
doi:10.1117/12.851288
fatcat:34322b57djgrzjyb3u45oegvnm
Matched filter stochastic background characterization for hyperspectral target detection
2005
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
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segmentation of image data into spatial or spectral subsets. ...
between target and non-target species in the detection statistic and ultimately improving thresholded target detection results. ...
This "target approach" method can be used both to exclude targets and to achieve greater multivariate normality (MVN). ...
doi:10.1117/12.605727
fatcat:vn4jedgc4fdvlalsnjins7kooq
Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity
2012
Journal of Electrical and Computer Engineering
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They differ in the way the background is characterized and in the method used for determining the difference between the current pixel and the background. ...
Global RX characterizes the background of the complete scene by a single multivariate normal probability density function. In many cases, this model is not appropriate for describing the background. ...
The MVSA matlab code, used in TLES, is kindly provided by Professor J. Bioucas on http://www.lx.it.pt/∼bioucas/code.htm. ...
doi:10.1155/2012/162106
fatcat:ytu4ydjazrbjjddowe5zxj53v4
Algorithms for Multispectral and Hyperspectral Image Analysis
2013
Journal of Electrical and Computer Engineering
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and spectral features [4], background modeling for anomaly detection [5, 6] , robust target detection techniques [7], lowdimensional representation, fusion of learning algorithms, the balance of statistical ...
e extensive performance analysis of these methods is presented in scenes with various backgrounds and different representative targets. ...
of spatial and spectral features [4] , background modeling for anomaly detection [5, 6] , robust target detection techniques [7] , lowdimensional representation, fusion of learning algorithms, the ...
doi:10.1155/2013/908906
fatcat:a2csgij2pfezrguys7uxzycwbq
Developing an algorithm for local anomaly detection based on spectral space window in hyperspectral image
2015
Earth Science Informatics
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The classic spatial local algorithms and proposed algorithm are compared by using real hyperspectral images from vehicle and aviation platforms. ...
This paper studies the local linear ideas in manifold learning, and an anomaly detection algorithm has been implemented based on the linear projections in a local area of spectral space. ...
The SSW-LPAD algorithm and OMIS dataset are used here. Usually, guard window G are determined by the targets, spatial size and image resolution. ...
doi:10.1007/s12145-014-0200-4
fatcat:sfawgah2ejbjzf3v2rbglyo6wy
Detection algorithms for hyperspectral imaging applications
2002
IEEE Signal Processing Magazine
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Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds ...
Detection and identification of military and civilian targets from airborne platforms using hyperspectral sensors is of great interest. ...
Also, use of clustering algorithms to derive more homogeneous background segments has been shown to improve detection performance in many cases [40] . ...
doi:10.1109/79.974724
fatcat:dnp3rjn23fgvzadhalmvghmkoy
Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
2021
Applied Sciences
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The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. ...
Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. ...
These pixels are then used for estimation of unknown parameters of the multivariate normal distribution: and . ...
doi:10.3390/app11114878
fatcat:jhyxr7gn2fexti5fqa37u2gff4
Interest Segmentation of Large Area Spectral Imagery for Analyst Assistance
2012
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Widely used methods of spectral clustering, target, and anomaly detection when applied to spectral imagery provide less than desirable results across sensor type, scene content, spectral and spatial resolutions ...
For this research, a variety of data driven algorithms for spectral image analysis are applied to spatial tiles of a large area scene. ...
Unlike target detection, the goal of large area search does not generally include identifying specific targets with known spatial characteristics or spectral signatures. ...
doi:10.1109/jstars.2012.2195298
fatcat:ftrhg4bipfbzdm5i2bnlbjdd6e
Detection of small changes in complex urban and industrial scenes using imaging spectroscopy
2010
2010 IEEE International Geoscience and Remote Sensing Symposium
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It was found that the use of a spatially adaptive detector greatly increases change-detection performance for both target detection and false alarm reduction. ...
INTRODUCTION Hyperspectral change detection has been proved to be a promising technique for detecting indiscernible targets in different background. ...
doi:10.1109/igarss.2010.5653711
dblp:conf/igarss/ShimoniHP10
fatcat:hclxpg2knngfbbriilqpeoxxna
Probabilistic anomaly detector for remotely sensed hyperspectral data
2014
Journal of Applied Remote Sensing
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The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most ...
suitable threshold for the separation of targets from the background. ...
the Target Blind Detection Test data sets. ...
doi:10.1117/1.jrs.8.083538
fatcat:7ftzgbu2nfcmrlp4npywiyqehi
Evaluation of gradient operators for hyperspectral image processing
2017
Final program and proceedings (Color and Imaging Conference)
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Gradient is an important image processing tool allowing to carry out edge detection, segmentation, and texture analysis. ...
This protocol is then used to evaluate the full-band gradient approaches, where the results suggest to improve the protocol to include more complexity. ...
Acknowledgment This work is partly supported by PatAttriMetro project of the French ERDF NUMERIC and the HyPerCept project funded by the Research Council of Norway. ...
doi:10.2352/issn.2169-2629.2017.25.182
fatcat:hsbibl6gone47l7w6fbiz64yye
Is there a best hyperspectral detection algorithm?
2009
SPIE Newsroom
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A large number of hyperspectral detection algorithms have been developed and used over the last two decades. ...
Some algorithms are based on highly sophisticated mathematical models and methods; others are derived using intuition and simple geometrical concepts. The purpose of this paper is threefold. ...
The detectability of a full pixel target depends on the spectral contrast between target and background. ...
doi:10.1117/2.1200906.1560
fatcat:ojub7iqp3fewtoq6ek352tveny
Is there a best hyperspectral detection algorithm?
2009
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
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The detectability of a full pixel target depends on the spectral contrast between target and background. ...
Therefore, it is not guaranteed that this more sophisticated model will lead to improved detection performance. ...
doi:10.1117/12.816917
fatcat:vxtv5psq2nbadikmaaug2v3ewy
Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing
2008
Journal of Real-Time Image Processing
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imager with high spatial and spectral resolution. ...
target detection and numerous other applications. ...
Anomaly detection algorithm The anomaly detection algorithm used here is based on a global multivariate normal mixture model representation of the background clutter, as discussed in [5] . ...
doi:10.1007/s11554-008-0105-x
fatcat:irrt4meefzdurpizcq4s6ckrrm
Hyperspectral Anomaly Detection Using Deep Learning: A Review
2022
Remote Sensing
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ...
It is assumed that the distribution of each segment is close to the multivariate Gaussian distribution, then the more abnormal targets appear, the larger the standard deviation of the corresponding segment ...
, and then perform abnormal target detection on the extracted features. ...
doi:10.3390/rs14091973
dblp:journals/remotesensing/HuXFDZJWHLZCWC22
fatcat:vwb3azo7cjgopaj7lrbwp6wzki
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