Abstract: A method is provided for estimating velocity of a moving object. The method includes generating a raw velocity for the moving object from a position change between a first image generated by a first sensor and a second image generated by a second sensor of a sensor assembly and selecting a plurality of stationary background features that are captured with the moving object in one or more images. A background misregistration value is generated from apparent movement of the plurality of stationary background features and is used to correct the raw velocity.

Abstract: Technology is provided for identifying concrete and/or asphalt (or other materials) in a multispectral satellite image that has multiple bands including a first set of bands from a first sensor and a second set of bands from a second sensor. The second sensor is at a different position on a focal plane as compared to the first sensor so that a single location depicted in the multispectral image will have been sensed at different times by the first sensor and the second sensor. The system identifies moving vehicles in the multispectral image and subsequently identifies sample pixels in the multispectral image that are near the moving vehicles. These pixels are high confidence samples of roads made of concrete and/or asphalt. Additional pixels are identified in the multispectral image having spectral characteristics that are within a threshold of spectral characteristics of the sample pixels. These additional pixels also depict concrete and/or asphalt.

Abstract: An automated system is provided for classifying materials in remotely-sensed imagery based on automated construction of a dynamic classifier—namely, a classifier that is automatically trained on the same image to which it is then subsequently applied. A first automated process identifies high confidence exemplars of each class using tailored classification techniques. This data is then used to train a supervised classification model (e.g., discriminant analysis), and the resultant classifier is applied to other pixels in the image that are unclassified or uncertain. Dynamic classification is automatically customized to the current image and can yield a more accurate and efficient material classification versus a static (image-independent) or manually trained classifier.

Abstract: Described herein are methods and systems for detecting clouds in satellite imagery captured using first and second sensor arrays that are carried by a satellite and physically offset from one another on the satellite. Movement mask data is produced based first image data and the second image data, obtained, respectively, using the first and second sensor arrays carried by the satellite. Cloud mask data is produced based on spectral information included in one of the first and second image data. Cloud detection data is produced based on the movement mask data and the cloud mask data, the cloud detection data indicating where it is likely, based on both the movement mask data and the cloud mask data, that one or more clouds are represented within one of the first and second image data. The cloud detection data can be used in various ways to account for the clouds included within the satellite imagery.

Abstract: Technology is provided for identifying concrete and/or asphalt (or other materials) in a multispectral satellite image that has multiple bands including a first set of bands from a first sensor and a second set of bands from a second sensor. The second sensor is at a different position on a focal plane as compared to the first sensor so that a single location depicted in the multispectral image will have been sensed at different times by the first sensor and the second sensor. The system identifies moving vehicles in the multispectral image and subsequently identifies sample pixels in the multispectral image that are near the moving vehicles. These pixels are high confidence samples of roads made of concrete and/or asphalt. Additional pixels are identified in the multispectral image having spectral characteristics that are within a threshold of spectral characteristics of the sample pixels. These additional pixels also depict concrete and/or asphalt.

Abstract: An automated system is provided for classifying materials in remotely-sensed imagery based on automated construction of a dynamic classifier—namely, a classifier that is automatically trained on the same image to which it is then subsequently applied. A first automated process identifies high confidence exemplars of each class using tailored classification techniques. This data is then used to train a supervised classification model (e.g., discriminant analysis), and the resultant classifier is applied to other pixels in the image that are unclassified or uncertain. Dynamic classification is automatically customized to the current image and can yield a more accurate and efficient material classification versus a static (image-independent) or manually trained classifier.

Abstract: Described herein are methods and systems for detecting clouds in satellite imagery captured using first and second sensor arrays that are carried by a satellite and physically offset from one another on the satellite. Movement mask data is produced based first image data and the second image data, obtained, respectively, using the first and second sensor arrays carried by the satellite. Cloud mask data is produced based on spectral information included one of the first and second image data. Cloud detection data is produced based on the movement mask data and the cloud mask data, the cloud detection data indicating where it is likely, based on both the movement mask data and the cloud mask data, that one or more clouds are represented within one of the first and second image data. The cloud detection data can be used in various ways to account for the clouds included within the satellite imagery.

Abstract: A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.

Abstract: Techniques for improving overhead image bathymetry include obtaining depth information from image data based on one or more of the spectral domain, the angular domain (e.g., stereo or photogrammetry), the temporal domain (e.g., monitoring the movement of waves in a body of water), or any other suitable domain, together with a priori information about the area of interest. These different pieces of depth information from the various different domains are combined together using any combination of Optimal Estimation and Continuity Constraints to improve the accuracy of the results.

Abstract: Techniques for improving overhead image bathymetry include obtaining depth information from image data based on one or more of the spectral domain, the angular domain (e.g., stereo or photogrammetry), the temporal domain (e.g., monitoring the movement of waves in a body of water), or any other suitable domain, together with a priori information about the area of interest. These different pieces of depth information from the various different domains are combined together using any combination of Optimal Estimation and Continuity Constraints to improve the accuracy of the results.

Abstract: Techniques for improving overhead image bathymetry include obtaining depth information from image data based on one or more of the spectral domain, the angular domain (e.g., stereo or photogrammetry), the temporal domain (e.g., monitoring the movement of waves in a body of water), or any other suitable domain, together with a priori information about the area of interest. These different pieces of depth information from the various different domains are combined together using any combination of Optimal Estimation and Continuity Constraints to improve the accuracy of the results.

Abstract: A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.

Abstract: A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.

Abstract: Techniques for improving overhead image bathymetry include obtaining depth information from image data based on one or more of the spectral domain, the angular domain (e.g., stereo or photogrammetry), the temporal domain (e.g., monitoring the movement of waves in a body of water), or any other suitable domain, together with a priori information about the area of interest. These different pieces of depth information from the various different domains are combined together using any combination of Optimal Estimation and Continuity Constraints to improve the accuracy of the results.

Abstract: A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.

Abstract: A technique for information retrieval includes parsing a corpus to identify a number of wordform instances within each document of the corpus. A weighted morpheme-by-document matrix is generated based at least in part on the number of wordform instances within each document of the corpus and based at least in part on a weighting function. The weighted morpheme-by-document matrix separately enumerates instances of stems and affixes. Additionally or alternatively, a term-by-term alignment matrix may be generated based at least in part on the number of wordform instances within each document of the corpus. At least one lower rank approximation matrix is generated by factorizing the weighted morpheme-by-document matrix and/or the term-by-term alignment matrix.

Abstract: A technique for information retrieval includes parsing a corpus to identify a number of wordform instances within each document of the corpus. A weighted morpheme-by-document matrix is generated based at least in part on the number of wordform instances within each document of the corpus and based at least in part on a weighting function. The weighted morpheme-by-document matrix separately enumerates instances of stems and affixes. Additionally or alternatively, a term-by-term alignment matrix may be generated based at least in part on the number of wordform instances within each document of the corpus. At least one lower rank approximation matrix is generated by factorizing the weighted morpheme-by-document matrix and/or the term-by-term alignment matrix.