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公开(公告)号：US08983797B2
公开(公告)日：2015-03-17
申请号：US13926394
申请日：2013-06-25
Applicant: Raytheon Company
Inventor： Paul M. Ingram, Jr. , Joseph C. Landry , John J. Coogan , Paul D. Shocklee
CPC classification number: G06F17/18 , G01S7/411 , G01S7/4802 , G01S13/9035 , G01S17/89
Abstract: In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.
Abstract translation: 在用于远程识别非朗伯式目标材料的方法的一个示例中，从以不同角度获取的至少两个不同组的图像中的每一个确定目标的光谱签名，并将其与用于 每个至少两个不同的角度。 预测的签名考虑到已知的候选材料的反射率各向异性，因此也考虑了辐射度。
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公开(公告)号：US11941878B2
公开(公告)日：2024-03-26
申请号：US17358850
申请日：2021-06-25
Applicant: Raytheon Company
Inventor： Grant B. Boroughs , John J. Coogan , Lisa A. McCoy
CPC classification number: G06V20/182 , G06V10/751 , G06V20/176 , G06V20/584 , G06V10/58 , G06V20/194
Abstract: A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.
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公开(公告)号：US11010639B2
公开(公告)日：2021-05-18
申请号：US16279212
申请日：2019-02-19
Applicant: Raytheon Company
Inventor： John J. Coogan , Stephen J. Schiller
Abstract: An angularly-dependent reflectance of a surface of an object is measured. Images are collected by a sensor at different sensor geometries and different light-source geometries. A point cloud is generated. The point cloud includes a location of a point, spectral band intensity values for the point, an azimuth and an elevation of the sensor, and an azimuth and an elevation of a light source. Raw pixel intensities of the object and surroundings of the object are converted to a surface reflectance of the object using specular array calibration (SPARC) targets. A three-dimensional (3D) location of each point in the point cloud is projected back to each image using metadata from the plurality of images, and spectral band values are assigned to each value in the point cloud, thereby resulting in a multi-angle spectral reflectance data set. A multi-angle surface-specific signature (MASS) is fitted to the multi-angle spectral reflectance data set, and the multi-angle surface-specific signature of the object and the surroundings of the object are stored into a spectral database. The spectral database is mined to find one or more of spatial and temporal anomalies in the plurality of images and patterns and correlations of the multi-angle surface-specific signature.
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公开(公告)号：US11513072B2
公开(公告)日：2022-11-29
申请号：US17199604
申请日：2021-03-12
Applicant: Raytheon Company
Inventor： Gary A. Frazier , John J. Coogan
Abstract: A real-time ablation sensor uses an optical detector, such as a spectrometer or radiometer, to detect ablation of a material, for example by detecting a signal indicative of ablation of the material, which may be an engineered material. The optical detector may detect reflected light, either from the material being ablated, or from products of the ablation, such as in the vicinity of the material being ablated. A light source may be used to provide light that is reflected by the material and/or the ablation products, with the reflected light received by the detector. The light may be of a selected wavelength or wavelengths, with the selection made in combination with the selection/configuration of the material to be ablated, and/or the selection/configuration of the optical detector.
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公开(公告)号：US20190258899A1
公开(公告)日：2019-08-22
申请号：US16279212
申请日：2019-02-19
Applicant: Raytheon Company
Inventor： John J. Coogan , Stephen J. Schiller
Abstract: An angularly-dependent reflectance of a surface of an object is measured. Images are collected by a sensor at different sensor geometries and different light-source geometries. A point cloud is generated. The point cloud includes a location of a point, spectral band intensity values for the point, an azimuth and an elevation of the sensor, and an azimuth and an elevation of a light source. Raw pixel intensities of the object and surroundings of the object are converted to a surface reflectance of the object using specular array calibration (SPARC) targets. A three-dimensional (3D) location of each point in the point cloud is projected back to each image using metadata from the plurality of images, and spectral band values are assigned to each value in the point cloud, thereby resulting in a multi-angle spectral reflectance data set. A multi-angle surface-specific signature (MASS) is fitted to the multi-angle spectral reflectance data set, and the multi-angle surface-specific signature of the object and the surroundings of the object are stored into a spectral database. The spectral database is mined to find one or more of spatial and temporal anomalies in the plurality of images and patterns and correlations of the multi-angle surface-specific signature.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20140253543A1
公开(公告)日：2014-09-11
申请号：US14201008
申请日：2014-03-07
Applicant: Raytheon Company
Inventor： Stephen J. Raif , John J. Coogan , Steven B. Seida , Paul L. Pryor , Carlton E. Nance , Wyatt D. Sharp, III , Jody D. Verret
IPC: G06T17/00
CPC classification number: G06T17/00 , G06F17/30241 , G06F17/30256 , G06T7/0002 , G06T17/05 , G06T2200/04 , G06T2207/10028 , G06T2207/30168 , G06T2210/56
Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.
Abstract translation: 从被动图像生成点云的系统和方法。 形成图像簇，其中每个图像簇包括从一组无源图像中选择的两个或更多个被动图像。 基于每个图像集群的性能预测分数，为每个图像集群预测可以从每个图像集群生成的点云的质量。 基于其性能预测分数，选择图像簇的子集用于进一步处理。 对于生成的每个点云确定特定任务质量得分，并选择具有最高质量得分的点云进行存储。
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公开(公告)号：US20220414376A1
公开(公告)日：2022-12-29
申请号：US17358850
申请日：2021-06-25
Applicant: Raytheon Company
Inventor： Grant B. Boroughs , John J. Coogan , Lisa A. McCoy
Abstract: A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.
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公开(公告)号：US20220291125A1
公开(公告)日：2022-09-15
申请号：US17199604
申请日：2021-03-12
Applicant: Raytheon Company
Inventor： Gary A. Frazier , John J. Coogan
Abstract: A real-time ablation sensor uses an optical detector, such as a spectrometer or radiometer, to detect ablation of a material, for example by detecting a signal indicative of ablation of the material, which may be an engineered material. The optical detector may detect reflected light, either from the material being ablated, or from products of the ablation, such as in the vicinity of the material being ablated. A light source may be used to provide light that is reflected by the material and/or the ablation products, with the reflected light received by the detector. The light may be of a selected wavelength or wavelengths, with the selection made in combination with the selection/configuration of the material to be ablated, and/or the selection/configuration of the optical detector.
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9.发明授权公开(公告)号：US10607104B1
公开(公告)日：2020-03-31
申请号：US16263482
申请日：2019-01-31
Applicant: Raytheon Company
Inventor： Joseph C. Landry , John J. Coogan
Abstract: A system and method for processing a daytime IR image to discriminate between solar glints and hotspots, where the latter represent man-made activity. Two spectrally distinct thermal wavelength bands are defined and respective spectral intensities are detected for a corresponding pixel in an image. A figure of merit is calculated as a function of the detected spectral intensities. The calculated figure of merit is compared to a predetermined rule to determine whether the corresponding pixel is a glint or a hotspot.
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10.发明授权公开(公告)号：US09430872B2
公开(公告)日：2016-08-30
申请号：US14201008
申请日：2014-03-07
Applicant: Raytheon Company
Inventor： Stephen J. Raif , John J. Coogan , Steven B. Seida , Paul L. Pryor , Carlton E. Nance , Wyatt D. Sharp, III , Jody D. Verret
CPC classification number: G06T17/00 , G06F17/30241 , G06F17/30256 , G06T7/0002 , G06T17/05 , G06T2200/04 , G06T2207/10028 , G06T2207/30168 , G06T2210/56
Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.
Abstract translation: 从被动图像生成点云的系统和方法。 形成图像簇，其中每个图像簇包括从一组无源图像中选择的两个或更多个被动图像。 基于每个图像集群的性能预测分数，为每个图像集群预测可以从每个图像集群生成的点云的质量。 基于其性能预测分数，选择图像簇的子集用于进一步处理。 对于生成的每个点云确定特定任务质量得分，并选择具有最高质量得分的点云进行存储。
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