Dimensionality Reduction (DR) has found many applications in hyperspectral image processing, e.g., data compression, endmember extraction. This paper investigates Projection Pursuit (PP)-based data dimensionality reduction where three approaches are developed. One is to use a Projection Index (PI) to produce projection vectors that can be used to generate Projection Index Components (PICs). It is a common practice that PP generally uses random initial conditions to produce PICs. As a result, when the same PP is performed in different times or different users at the same time, the resulting PICs are generally not the same. In order to resolve this issue, two approaches are proposed. One is referred to as PI-based PRioritized PP (PI-PRPP) which uses a PI as a criterion to prioritize PICs that are produced by any component analysis, for example, Principal Components Analysis (PCA) or Independent Component Analysis. The other approach is called Initialization-Driven PP (ID-PP) which specifies an appropriate set of initial conditions that allows PP to not only produce PICs in the same order but also the same PICs regardless of how many times PP is run or who runs the PP.
{"title":"Projection pursuit-based dimensionality reduction","authors":"H. Safavi, Chein-I. Chang","doi":"10.1117/12.778014","DOIUrl":"https://doi.org/10.1117/12.778014","url":null,"abstract":"Dimensionality Reduction (DR) has found many applications in hyperspectral image processing, e.g., data compression, endmember extraction. This paper investigates Projection Pursuit (PP)-based data dimensionality reduction where three approaches are developed. One is to use a Projection Index (PI) to produce projection vectors that can be used to generate Projection Index Components (PICs). It is a common practice that PP generally uses random initial conditions to produce PICs. As a result, when the same PP is performed in different times or different users at the same time, the resulting PICs are generally not the same. In order to resolve this issue, two approaches are proposed. One is referred to as PI-based PRioritized PP (PI-PRPP) which uses a PI as a criterion to prioritize PICs that are produced by any component analysis, for example, Principal Components Analysis (PCA) or Independent Component Analysis. The other approach is called Initialization-Driven PP (ID-PP) which specifies an appropriate set of initial conditions that allows PP to not only produce PICs in the same order but also the same PICs regardless of how many times PP is run or who runs the PP.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116037746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present SWIR advantages for realizing low-power, high-speed and small size search-detect and tracking optical systems. The characteristics of low-clutter, and robustness of the target observables when atmospheric interference occurs are discussed in detail. Next - we present the SWIR building blocks developed in order to allow for the detection systems to be built.
{"title":"SWIR imager design and building blocks for automatic detection system","authors":"G. Tidhar, Y. Ben-Horin, Harel Shefaram","doi":"10.1117/12.780797","DOIUrl":"https://doi.org/10.1117/12.780797","url":null,"abstract":"We present SWIR advantages for realizing low-power, high-speed and small size search-detect and tracking optical systems. The characteristics of low-clutter, and robustness of the target observables when atmospheric interference occurs are discussed in detail. Next - we present the SWIR building blocks developed in order to allow for the detection systems to be built.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126966246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Duclos, J. Lonnoy, Q. Guillerm, F. Jurie, S. Herbin, E. D'Angelo
The last five years have seen a renewal of Automatic Target Recognition applications, mainly because of the latest advances in machine learning techniques. In this context, large collections of image datasets are essential for training algorithms as well as for their evaluation. Indeed, the recent proliferation of recognition algorithms, generally applied to slightly different problems, make their comparisons through clean evaluation campaigns necessary. The ROBIN project tries to fulfil these two needs by putting unclassified datasets, ground truths, competitions and metrics for the evaluation of ATR algorithms at the disposition of the scientific community. The scope of this project includes single and multi-class generic target detection and generic target recognition, in military and security contexts. From our knowledge, it is the first time that a database of this importance (several hundred thousands of visible and infrared hand annotated images) has been publicly released. Funded by the French Ministry of Defence (DGA) and by the French Ministry of Research, ROBIN is one of the ten Techno-vision projects. Techno-vision is a large and ambitious government initiative for building evaluation means for computer vision technologies, for various application contexts. ROBIN's consortium includes major companies and research centres involved in Computer Vision R&D in the field of defence: Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES. This paper, which first gives an overview of the whole project, is focused on one of ROBIN's key competitions, the SAGEM Defence Security database. This dataset contains more than eight hundred ground and aerial infrared images of six different vehicles in cluttered scenes including distracters. Two different sets of data are available for each target. The first set includes different views of each vehicle at close range in a "simple" background, and can be used to train algorithms. The second set contains many views of the same vehicle in different contexts and situations simulating operational scenarios.
{"title":"ROBIN: a platform for evaluating automatic target recognition algorithms: I. Overview of the project and presentation of the SAGEM DS competition","authors":"D. Duclos, J. Lonnoy, Q. Guillerm, F. Jurie, S. Herbin, E. D'Angelo","doi":"10.1117/12.777501","DOIUrl":"https://doi.org/10.1117/12.777501","url":null,"abstract":"The last five years have seen a renewal of Automatic Target Recognition applications, mainly because of the latest advances in machine learning techniques. In this context, large collections of image datasets are essential for training algorithms as well as for their evaluation. Indeed, the recent proliferation of recognition algorithms, generally applied to slightly different problems, make their comparisons through clean evaluation campaigns necessary. The ROBIN project tries to fulfil these two needs by putting unclassified datasets, ground truths, competitions and metrics for the evaluation of ATR algorithms at the disposition of the scientific community. The scope of this project includes single and multi-class generic target detection and generic target recognition, in military and security contexts. From our knowledge, it is the first time that a database of this importance (several hundred thousands of visible and infrared hand annotated images) has been publicly released. Funded by the French Ministry of Defence (DGA) and by the French Ministry of Research, ROBIN is one of the ten Techno-vision projects. Techno-vision is a large and ambitious government initiative for building evaluation means for computer vision technologies, for various application contexts. ROBIN's consortium includes major companies and research centres involved in Computer Vision R&D in the field of defence: Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES. This paper, which first gives an overview of the whole project, is focused on one of ROBIN's key competitions, the SAGEM Defence Security database. This dataset contains more than eight hundred ground and aerial infrared images of six different vehicles in cluttered scenes including distracters. Two different sets of data are available for each target. The first set includes different views of each vehicle at close range in a \"simple\" background, and can be used to train algorithms. The second set contains many views of the same vehicle in different contexts and situations simulating operational scenarios.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117307546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Component Analysis (CA) has found many applications in remote sensing image processing. Two major component analyses are of particular interest, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) which have been widely used in signal processing. While the PCA de-correlates data samples via 2nd order statistics in a set of Principal Components (PCs), the ICA represents data samples via statistical independency in a set of statistically Independent Components (ICs). However, in order to for component analyses to be effective, the number of components to be generated, p must be sufficient for data analysis. Unfortunately, in MultiSpectral Imagery (MSI) p seems to be small, while p in HyperSpectral Imagery (HSI) seems too large. Interestingly, very little has been reported on how to deal with this issue when p is too small or too large. This paper investigates this issue. When p is too small, two approaches are developed to mitigate the problem. One is Band Expansion Process (BEP) which augments original data band dimensionality by producing additional bands via a set of nonlinear functions. The other is a kernel-based approach, referred to as Kernel-based PCA (K-PCA) which maps features in the original data space to a higher dimensional feature space via a set of nonlinear kernel. While both approaches make attempts to resolve the issue of a small p using a set of nonlinear functions, their design rationales are completely different, particularly they are not correlated. As for a large p such as HSI, a recently developed Virtual Dimensionality (VD) can be used for this purpose where the VD was originally developed to estimate number of spectrally distinct signatures. If we assume one spectrally distinct signature can be accommodated by one component, the value of p can be actually determined by the VD. Finally, experiments are conducted to explore and evaluate the utility of component analyses, specifically, PCA and ICA using BEP and K-PCA for MSI and VD for HSI.
{"title":"Exploration of component analysis in multi/hyperspectral image processing","authors":"Keng-Hao Liu, Chein-I. Chang","doi":"10.1117/12.782219","DOIUrl":"https://doi.org/10.1117/12.782219","url":null,"abstract":"Component Analysis (CA) has found many applications in remote sensing image processing. Two major component analyses are of particular interest, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) which have been widely used in signal processing. While the PCA de-correlates data samples via 2nd order statistics in a set of Principal Components (PCs), the ICA represents data samples via statistical independency in a set of statistically Independent Components (ICs). However, in order to for component analyses to be effective, the number of components to be generated, p must be sufficient for data analysis. Unfortunately, in MultiSpectral Imagery (MSI) p seems to be small, while p in HyperSpectral Imagery (HSI) seems too large. Interestingly, very little has been reported on how to deal with this issue when p is too small or too large. This paper investigates this issue. When p is too small, two approaches are developed to mitigate the problem. One is Band Expansion Process (BEP) which augments original data band dimensionality by producing additional bands via a set of nonlinear functions. The other is a kernel-based approach, referred to as Kernel-based PCA (K-PCA) which maps features in the original data space to a higher dimensional feature space via a set of nonlinear kernel. While both approaches make attempts to resolve the issue of a small p using a set of nonlinear functions, their design rationales are completely different, particularly they are not correlated. As for a large p such as HSI, a recently developed Virtual Dimensionality (VD) can be used for this purpose where the VD was originally developed to estimate number of spectrally distinct signatures. If we assume one spectrally distinct signature can be accommodated by one component, the value of p can be actually determined by the VD. Finally, experiments are conducted to explore and evaluate the utility of component analyses, specifically, PCA and ICA using BEP and K-PCA for MSI and VD for HSI.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129694442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Space Dynamics Laboratory (SDL) has developed an FPGA-based hyperspectral demonstration compression system. The system consists of two boards: the first board performs a decorrelation process using a 5/3 wavelet; the second board performs the JPEG 2000 image compression. The hardware and firmware design of this system is described here and data is presented that shows the results of compressed hyperspectral data cubes containing various types of image content. This paper presents the importance of bit rate control among the individual spectral bands. Some of the theory for basing bit rate control on JPEG 2000 compression, bit rate control based on the 5/3 wavelet, as well as advantages and disadvantages of each method are discussed. Concepts for developing hyperspectral image compression technology for systems that can be used for remote sensing in real applications are also presented.
{"title":"An FPGA-based demonstration hyperspectral image compression system","authors":"T. L. Woolston, G. Bingham, Niel Holt, G. Wada","doi":"10.1117/12.776900","DOIUrl":"https://doi.org/10.1117/12.776900","url":null,"abstract":"The Space Dynamics Laboratory (SDL) has developed an FPGA-based hyperspectral demonstration compression system. The system consists of two boards: the first board performs a decorrelation process using a 5/3 wavelet; the second board performs the JPEG 2000 image compression. The hardware and firmware design of this system is described here and data is presented that shows the results of compressed hyperspectral data cubes containing various types of image content. This paper presents the importance of bit rate control among the individual spectral bands. Some of the theory for basing bit rate control on JPEG 2000 compression, bit rate control based on the 5/3 wavelet, as well as advantages and disadvantages of each method are discussed. Concepts for developing hyperspectral image compression technology for systems that can be used for remote sensing in real applications are also presented.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125397792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Stevens, F. Sadjadi, Jacob R. Braegelmann, Aaron M. Cordes, R. L. Nelson
Small Unmanned Aerial Vehicles (UAVs) are increasingly being used in-theater to provide low-cost, low-profile aerial reconnaissance and surveillance capabilities. However, inherent platform limitations on size, weight, and power restrict the ability to provide sensors and communications which can present high-resolution imagery to the end-user. This paper discusses methods to alleviate this restriction by performing on-board pre-processing of high resolution images and downlinking the post-processed imagery. This has the added benefit of reducing the workload for a warfighter who is already heavily taxed by other duties.
{"title":"Small unmanned aerial vehicle (UAV) real-time intelligence, surveillance, and reconnaissance (ISR) using onboard pre-processing","authors":"R. Stevens, F. Sadjadi, Jacob R. Braegelmann, Aaron M. Cordes, R. L. Nelson","doi":"10.1117/12.780302","DOIUrl":"https://doi.org/10.1117/12.780302","url":null,"abstract":"Small Unmanned Aerial Vehicles (UAVs) are increasingly being used in-theater to provide low-cost, low-profile aerial reconnaissance and surveillance capabilities. However, inherent platform limitations on size, weight, and power restrict the ability to provide sensors and communications which can present high-resolution imagery to the end-user. This paper discusses methods to alleviate this restriction by performing on-board pre-processing of high resolution images and downlinking the post-processed imagery. This has the added benefit of reducing the workload for a warfighter who is already heavily taxed by other duties.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133943569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Plis, H. Kim, J. B. Rodriguez, G. Bishop, Y. Sharma, A. Khoshakhlagh, L. Dawson, J. Bundas, R. Cook, D. Burrows, R. Dennis, K. Patnaude, A. Reisinger, M. Sundaram, S. Krishna
The development of type-II InAs/(In,Ga)Sb superlattice (SL) detectors with nBn design for single-color and dual-color operation in MWIR and LWIR spectral regions are discussed. First, a 320 x 256 focal plane array (FPA) with cutoff wavelength of 4.2 μm at 77K with average value of dark current density equal to 1 x 10-7 A/cm2 at Vb=0.7V (77 K) is reported. FPA reveals NEDT values of 23.8 mK for 16.3 ms integration time and f/4 optics. At 77K, the peak responsivity and detectivity of FPA are estimated, respectively, to be 1.5 A/W and 6.4 x 1011 Jones, at 4 μm. Next, implementation of the nBn concept on design of SL LWIR detectors is presented. The fabrication of single element nBn based long wave infrared (LWIR ) with λc ~ 8.0 μm at Vb = +0.9 V and T = 100K detectors are reported. The bias dependent polarity can be exploited to obtain two color response (λc1 ~ 3.5 μm and λc2 ~ 8.0 μm) under different polarity of applied bias. The design and fabrication of this two color detector is presented. The dual band response (λc1 ~ 4.5 μm and λc2 ~ 8 μm) is achieved by changing the polarity of applied bias. The spectral response cutoff wavelength shifts from MWIR to LWIR when the applied bias voltage varies within a very small bias range (~100 mV).
{"title":"nBn based infrared detectors using type-II InAs/(In,Ga)Sb superlattices","authors":"E. Plis, H. Kim, J. B. Rodriguez, G. Bishop, Y. Sharma, A. Khoshakhlagh, L. Dawson, J. Bundas, R. Cook, D. Burrows, R. Dennis, K. Patnaude, A. Reisinger, M. Sundaram, S. Krishna","doi":"10.1117/12.780375","DOIUrl":"https://doi.org/10.1117/12.780375","url":null,"abstract":"The development of type-II InAs/(In,Ga)Sb superlattice (SL) detectors with nBn design for single-color and dual-color operation in MWIR and LWIR spectral regions are discussed. First, a 320 x 256 focal plane array (FPA) with cutoff wavelength of 4.2 μm at 77K with average value of dark current density equal to 1 x 10-7 A/cm2 at Vb=0.7V (77 K) is reported. FPA reveals NEDT values of 23.8 mK for 16.3 ms integration time and f/4 optics. At 77K, the peak responsivity and detectivity of FPA are estimated, respectively, to be 1.5 A/W and 6.4 x 1011 Jones, at 4 μm. Next, implementation of the nBn concept on design of SL LWIR detectors is presented. The fabrication of single element nBn based long wave infrared (LWIR ) with λc ~ 8.0 μm at Vb = +0.9 V and T = 100K detectors are reported. The bias dependent polarity can be exploited to obtain two color response (λc1 ~ 3.5 μm and λc2 ~ 8.0 μm) under different polarity of applied bias. The design and fabrication of this two color detector is presented. The dual band response (λc1 ~ 4.5 μm and λc2 ~ 8 μm) is achieved by changing the polarity of applied bias. The spectral response cutoff wavelength shifts from MWIR to LWIR when the applied bias voltage varies within a very small bias range (~100 mV).","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127732782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Ragothaman, Abhijit Mahalanobis, R. Muise, W. Mikhael
The Rayleigh Quotient Quadratic Correlation Filter (RQQCF) has been used to achieve very good performance for Automatic Target Detection/Recognition. The filter coefficients are obtained as the solution that maximizes a class separation metric, thus resulting in optimal performance. Recently, a transform domain approach was presented for ATR using the RQQCF called the Transform Domain RQQCF (TDRQQCF). The TDRQQCF considerably reduced the computational complexity and storage requirements, by compressing the target and clutter data used in designing the QCF. In addition, the TDRQQCF approach was able to produce larger responses when the filter was correlated with target and clutter images. This was achieved while maintaining the excellent recognition accuracy of the original spatial domain RQQCF algorithm. The computation of the RQQCF and the TDRQQCF involve the inverse of the term A1 = Rx + Ry where Rx and Ry are the sample autocorrelation matrices for targets and clutter respectively. It can be conjectured that the TDRQQCF approach is equivalent to regularizing A1. A common regularization approach involves performing the Eigenvalue Decomposition (EVD) of A1, setting some small eigenvalues to zero, and then reconstructing A1, which is now expected to be better conditioned. In this paper, this regularization approach is investigated, and compared to the TDRQQCF.
{"title":"A performance comparison of the transform domain Rayleigh quotient quadratic correlation filter (TDRQQCF) approach to the regularized RQQCF","authors":"P. Ragothaman, Abhijit Mahalanobis, R. Muise, W. Mikhael","doi":"10.1117/12.784055","DOIUrl":"https://doi.org/10.1117/12.784055","url":null,"abstract":"The Rayleigh Quotient Quadratic Correlation Filter (RQQCF) has been used to achieve very good performance for Automatic Target Detection/Recognition. The filter coefficients are obtained as the solution that maximizes a class separation metric, thus resulting in optimal performance. Recently, a transform domain approach was presented for ATR using the RQQCF called the Transform Domain RQQCF (TDRQQCF). The TDRQQCF considerably reduced the computational complexity and storage requirements, by compressing the target and clutter data used in designing the QCF. In addition, the TDRQQCF approach was able to produce larger responses when the filter was correlated with target and clutter images. This was achieved while maintaining the excellent recognition accuracy of the original spatial domain RQQCF algorithm. The computation of the RQQCF and the TDRQQCF involve the inverse of the term A1 = Rx + Ry where Rx and Ry are the sample autocorrelation matrices for targets and clutter respectively. It can be conjectured that the TDRQQCF approach is equivalent to regularizing A1. A common regularization approach involves performing the Eigenvalue Decomposition (EVD) of A1, setting some small eigenvalues to zero, and then reconstructing A1, which is now expected to be better conditioned. In this paper, this regularization approach is investigated, and compared to the TDRQQCF.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130359460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Partee, C. Paul, M. Sartor, J. West, N. Wichowski, B. McIntyre
Image Intensifier Tube (IIT) technology remains a critical component of the warfighter's arsenal. However, even after six decades of fielded systems most IIT inspections are accomplished relying on human judgment and round-robin calibration techniques. We report on the Automated Intensifier Measurement System (AIMS), a NIST-traceable, calibratable, machine vision system developed to produce automated, quantifiable, reproducible results on eight of the major IIT inspections: (1) Useful Diameter, (2) Modulation Transfer Function, (3) Gross Distortion, (4) Shear Distortion, (5) Bright Spot, (6) Dark Spot, (7) Gain and (8) Uniformity. The overall architecture of the system and a description of the algorithms required for each test is presented. Translation from the anthropocentric MIL-PRF-A3256363D(CR) OMNI VII Military Specification to measurable quantities (with appropriate uncertainties) is described. The NIST-traceable system uncertainties associated with each measurement is reported; in all cases AIMS measures quantities associated with the above tests to more precision than current industry practice. Issues with the current industry standard equipment and testing methods are also identified. Future work, which will include additional inspections, is discussed.
图像增强管(IIT)技术仍然是作战人员武器库的关键组成部分。然而，即使经过60年的现场系统，大多数IIT检查仍然依靠人工判断和循环校准技术完成。我们报告了自动增强测量系统(AIMS)，这是一个nist可追溯，可校准的机器视觉系统，用于在IIT的八个主要检查中产生自动化，可量化，可重复的结果:(1)有用直径，(2)调制传递函数，(3)总畸变，(4)剪切畸变，(5)亮点，(6)暗斑，(7)增益和(8)均匀性。给出了系统的总体架构和每个测试所需算法的描述。描述了从以人类为中心的MIL-PRF-A3256363D(CR) OMNI VII军事规范到可测量量(具有适当的不确定度)的翻译。报告了与每次测量相关的nist可追溯系统不确定度;在所有情况下，AIMS测量与上述测试相关的数量比目前的工业实践更精确。还确定了当前行业标准设备和测试方法的问题。讨论了今后的工作，其中将包括更多的视察。
{"title":"Automated intensifier tube measuring system","authors":"J. Partee, C. Paul, M. Sartor, J. West, N. Wichowski, B. McIntyre","doi":"10.1117/12.771384","DOIUrl":"https://doi.org/10.1117/12.771384","url":null,"abstract":"Image Intensifier Tube (IIT) technology remains a critical component of the warfighter's arsenal. However, even after six decades of fielded systems most IIT inspections are accomplished relying on human judgment and round-robin calibration techniques. We report on the Automated Intensifier Measurement System (AIMS), a NIST-traceable, calibratable, machine vision system developed to produce automated, quantifiable, reproducible results on eight of the major IIT inspections: (1) Useful Diameter, (2) Modulation Transfer Function, (3) Gross Distortion, (4) Shear Distortion, (5) Bright Spot, (6) Dark Spot, (7) Gain and (8) Uniformity. The overall architecture of the system and a description of the algorithms required for each test is presented. Translation from the anthropocentric MIL-PRF-A3256363D(CR) OMNI VII Military Specification to measurable quantities (with appropriate uncertainties) is described. The NIST-traceable system uncertainties associated with each measurement is reported; in all cases AIMS measures quantities associated with the above tests to more precision than current industry practice. Issues with the current industry standard equipment and testing methods are also identified. Future work, which will include additional inspections, is discussed.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121670270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In our previous paper, it has been demonstrated that principal component analysis (PCA) can outperform discrete wavelet transform (DWT) in spectral coding for hyperspectral image compression and a superior rate distortion performance can be provided in conjunction with 2-dimensional (2D) spatial coding using JPEG2000. The resulting compression algorithm is denoted as PCA+JPEG2000. In this paper, we further investigate how the data size (i.e., spatial and spectral size) influences the performance of PCA+JPEG2000 and provide a rule of thumb for PCA+JPEG2000 to perform appropriately. We will also show that using a subset of principal components (PCs) (the resulting algorithm is denoted as SubPCA+JPEG2000) can always yield a better rate distortion performance than PCA+JPEG2000 with all the PCs being preserved for compression.
{"title":"Improving the performance of PCA and JPEG2000 for hyperspectral image compression","authors":"Q. Du, Wei Zhu","doi":"10.1117/12.777317","DOIUrl":"https://doi.org/10.1117/12.777317","url":null,"abstract":"In our previous paper, it has been demonstrated that principal component analysis (PCA) can outperform discrete wavelet transform (DWT) in spectral coding for hyperspectral image compression and a superior rate distortion performance can be provided in conjunction with 2-dimensional (2D) spatial coding using JPEG2000. The resulting compression algorithm is denoted as PCA+JPEG2000. In this paper, we further investigate how the data size (i.e., spatial and spectral size) influences the performance of PCA+JPEG2000 and provide a rule of thumb for PCA+JPEG2000 to perform appropriately. We will also show that using a subset of principal components (PCs) (the resulting algorithm is denoted as SubPCA+JPEG2000) can always yield a better rate distortion performance than PCA+JPEG2000 with all the PCs being preserved for compression.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121822892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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