Mr. Gary J. Bishop Profile

Gary J. Bishop

at BAE Systems

SPIE Involvement:

Editor | Author

Proceedings Article | 12 October 2010 Paper

Detection of disturbed earth using hyperspectral LWIR imaging data

Wendy Hubbard, Gary Bishop, Jean-Philippe Gagnon, Philippe Lagueux, Sion Hannuna, Neill Campbell

Proceedings Volume 7835, 78350T (2010) https://doi.org/10.1117/12.869838

KEYWORDS: Long wavelength infrared, Sensors, Infrared imaging, Infrared sensors, Hyperspectral imaging, Infrared radiation, Calibration, Soil science, Imaging systems, Improvised explosive devices

Read Abstract +

Proceedings Article | 3 October 2008 Paper

Spectral tracking of objects in real time

Gary Bishop, Ainsley Killey, Matthew Porter, Adrian Blagg

Proceedings Volume 7119, 71190D (2008) https://doi.org/10.1117/12.802202

KEYWORDS: Optical filters, Reflectivity, Image filtering, Calibration, Target detection, Sensors, Detection and tracking algorithms, Hyperspectral imaging, Short wave infrared radiation, Image processing

Read Abstract +

Proceedings Article | 2 October 2008 Paper

Multispectral-polarimetric sensing for detection of difficult targets

Wendy Hubbard, Gary Bishop, Trevor Gowen, Daniel Hayter, Greg Innes

Proceedings Volume 7113, 71130L (2008) https://doi.org/10.1117/12.801889

KEYWORDS: Target detection, Sensors, Short wave infrared radiation, Polarimetry, Image processing, Multispectral imaging, Image sensors, Modeling, Cameras, Polarization

Read Abstract +

Proceedings Article | 8 October 2007 Paper

Temporal performance of spectral matched filtering techniques

Adrian Blagg, Gary Bishop, Ainsley Killey, Matthew Porter

Proceedings Volume 6741, 67410U (2007) https://doi.org/10.1117/12.778859

KEYWORDS: Optical filters, Image filtering, Reflectivity, Hyperspectral imaging, Detection and tracking algorithms, Digital filtering, Algorithm development, Imaging systems, Calibration, RGB color model

Read Abstract +

Proceedings Article | 5 October 2006 Paper

Hyperspectral multiple approach fusion for the long-range detection of low observable objects: MUF2

Peter W. Yuen, G. Bishop

Proceedings Volume 6396, 63960C (2006) https://doi.org/10.1117/12.686818

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Modeling, Data modeling, Statistical modeling, Vegetation, Image segmentation, Artificial intelligence, Statistical analysis

Read Abstract +

Highly efficient target detection algorithms in hyperspectral remote sensing technology, particularly for the long range detection of very low observable objects which exhibit extremely small detection cross sections, are in great demand. This is more so for a near or real time application. This paper is concerned with global anomaly detections (GAD), and conventional methods to achieve better detection using multiple approach fusion (MAF), which fuses detection outputs from various detectors using either logical operators, or, via a model based estimation of the joint detection statistics from all detectors, is found to be not good enough. This work emphasises the need to integrate a more comprehensive background modelling into the GAD to develop a robust anomaly detector (AD). Then, the detection output from this detector is fused with other detectors via MAF for a further improvement of detection performance. The MUF2 algorithm is formulated exactly using this 2-level fusion mechanism, in which mixture modelling and spectral unmixing fusion have been employed. The significance of background modelling in GAD has been highlighted in this work using real data. The result has shown a factor of 2-5 reduction in detection performance when a very small amount of target pixels (~0.1%) is misclassified as background. This is because anomalies are defined with reference to a model of the background, and subsequently two new background classification techniques have been proposed in this work. The effectiveness of the MUF2 has been assessed using three representative data sets which contain various different types of targets, ranging from vehicles to small plates embedded in backgrounds with various degrees of homogeneity. The performance of MUF2 has been shown to be more superior than the conventional GAD frequently in orders of magnitude, regardless of the background homogeneity and target types. The current version of the MUF2 is run under Matlab and it takes ~2 minutes to process a 20K pixel imagery. This work forms part of the research programme supported by the EMRS DTC established by the UK MOD.

Proceedings Article | 21 October 2005 Paper

The effectiveness and limitations of geometric and statistical spectral unmixing techniques for subpixel target detection in hyperspectral remote sensing

Peter WT Yuen, A. Blagg, G. Bishop

Proceedings Volume 5988, 59880C (2005) https://doi.org/10.1117/12.629864

KEYWORDS: Evolutionary algorithms, Independent component analysis, Artificial intelligence, Expectation maximization algorithms, Remote sensing, Statistical analysis, Hyperspectral imaging, Modeling, Error analysis, Clouds

Read Abstract +

In the literature of spectral unmixing (SU), particularly for remote sensing applications, there are claims that both geometric and statistical techniques using independency as cost functions^1-4, are very applicable for analysing hyperspectral imagery. These claims are vigorously examined and verified in this paper, using sets of simulated and real data. The objective is to study how effective these two SU approaches are with respected to the modality and independency of the source data. The data sets are carefully designed such that only one parameter is varied at a time. The 'goodness' of the unmixed result is judged by using the well-known Amari index (AI), together with a 3D visualisation of the deduced simplex in eigenvector space. A total of seven different algorithms, of which one is geometric and the others are statistically independent based have been studied. Two of the statistical algorithms use non-negative constraint of modelling errors (NMF & NNICA) as cost functions and the other four employ the independent component analysis (ICA) principle to minimise mutual information (MI) as the objective function. The result has shown that, the ICA based statistical technique is very effective to find the correct endmember (EM) even for the highly intermixed imagery, provided that the sources are completely independent. Modality of the data source is found to only have a second order impact on the unmixing capabilities of ICA based algorithms. All ICA based algorithms are seen to fail when the MI of sources are above 1, and the NMF type of algorithms are found even more sensitive to the dependency of sources. Typical independency of species found in the natural environment is in the range of 15-30. This indicates that, conventional statistical ICA and matrix factorisation (MF) techniques, are really not very suitable for the spectral unmixing of hyperspectral (HSI) data. Future work is proposed to investigate the idea of a dependent component clustering technique, a fused geometric and statistical approach, and couple these with a modification of the conventional ICA based algorithms to model the independency of the mixing, rather than the sources. This work formulates part of the research programme supported by the EMRS DTC established by the UK MOD.

Proceedings Article | 8 December 2004 Paper

Adaptive feature extractions techniques for subpixel target detections in hyperspectral remote sensing

Peter Yuen, Gary Bishop

Proceedings Volume 5613, (2004) https://doi.org/10.1117/12.578222

KEYWORDS: Target detection, Independent component analysis, Feature extraction, Optical filters, Detection and tracking algorithms, Remote sensing, Scene simulation, Sensors, Expectation maximization algorithms, Hyperspectral target detection

Read Abstract +

Proceedings Article | 8 December 2004 Paper

Enhancements of target detection using atmospheric correction preprocessing techniques in hyperspectral remote sensing

Peter Yuen, Gary Bishop

Proceedings Volume 5613, (2004) https://doi.org/10.1117/12.578688

KEYWORDS: Reflectivity, Atmospheric corrections, Atmospheric particles, Atmospheric modeling, Remote sensing, Target detection, Sensors, Visibility, Scattering, Rayleigh scattering

Read Abstract +

Proceedings Article | 5 September 2003 Paper

SIRUS spectral signature analysis code

Gary Bishop, Mike Caola, Rachel Geatches, Nick Roberts

Proceedings Volume 5075, (2003) https://doi.org/10.1117/12.487716

KEYWORDS: Sensors, Reflectivity, Atmospheric modeling, Infrared signatures, Systems modeling, Infrared technology, Earth's atmosphere, High dynamic range imaging, Bidirectional reflectance transmission function, Rockets

Read Abstract +

Proceedings Article | 16 September 1999 Paper

Determination of exhaust composition in turbine test beds by FTIR emission spectrometry

Klaus Schaefer, Joerg Heland, Erwin Lindermeir, Moira Hilton, Gary Bishop, Johanna Vally, Peter Wiesen, Dave Lister, Marc Bernard

Proceedings Volume 3821, (1999) https://doi.org/10.1117/12.364180

KEYWORDS: FT-IR spectroscopy, Spectroscopy, Carbon monoxide, Absorption, Temperature metrology, Mirrors, Calibration, Infrared radiation, NOx, Carbon dioxide

Read Abstract +

Proceedings Article | 16 September 1999 Paper

Evaluation of FTIR emission spectrometry for the determination of turbine exhaust composition in test beds

Klaus Schaefer, Joerg Heland, Dave Lister, Erwin Lindermeir, Moira Hilton, Gary Bishop, Peter Wiesen, Marc Bernard

Proceedings Volume 3821, (1999) https://doi.org/10.1117/12.364205

KEYWORDS: FT-IR spectroscopy, Spectroscopy, Calibration, Carbon monoxide, Spectrometers, Radio optics, Mirrors, Temperature metrology, Data modeling, Absorption

Read Abstract +

Proceedings Article | 23 May 1997 Paper

AEROJET: nonintrusive measurements of aircraft engine exhaust emissions

Klaus Schaefer, Joerg Heland, Roger Burrows, John Black, Marc Bernard, Gary Bishop, Volker Tank, Erwin Lindermeir, Dave Lister, Robert Falk, Peter Wiesen, Moira Hilton

Proceedings Volume 3108, (1997) https://doi.org/10.1117/12.274773

KEYWORDS: Spectroscopy, Temperature metrology, FT-IR spectroscopy, Calibration, Gases, Absorption, Environmental sensing, Mirrors, Carbon monoxide, Pollution control

Read Abstract +

Showing 5 of 12 publications

SPIE Conference Volume | 31 October 2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II

SPIE Conference Volume | 31 October 2013

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing

SPIE Conference Volume | 20 November 2012

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI

SPIE Conference Volume | 5 October 2011

Electro-Optical Remote Sensing, Photonic Technologies, and Applications V

SPIE Conference Volume | 8 October 2010

Electro-Optical Remote Sensing, Photonic Technologies, and Applications IV

Showing 5 of 6 publications

Conference Committee Involvement (6)