Harris Corporation

Morteza Akbari

Mark Donald Rahmes

Tariq Bakir

Ronald A Riley

Adrian M Peter

Mark Ernest Blue

Mike McGonagle

Patrick Kelley

Timothy B Faulkner

Jay Hackett

Gnana Bhaskar Tenali

Brian Hicks

Stephan Walker Miller

John T Cameron

Jeffrey W Houser

J Hartan Yates

A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, determining a first fractal pattern based upon the pre-heating data, and heating the sample with RF power to cause additional fracturing in the sample. The method may include generating post-heating data associated with additional fracturing in the sample after heating with RF power, determining a second fractal pattern based upon the post-heating data, detecting change between the first fractal pattern and the second fractal pattern, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Method for hydrocarbon recovery with a fractal pattern and related apparatus

Method for identifying objects within a three-dimensional point cloud data set. The method includes a fractal analysis () on a data set where the data set is comprised of data points () having positions distributed in three-dimensions. The fractal analysis facilitates identification of one or more object classes. The object class specifies a category of physical object. A phase congruency analysis () is then performed on the data set based on the object class identified by the fractal analysis. The phase congruency analysis is advantageously performed on an interpolated, noise reduced version of the data set (which can be obtained prior to performing the phase congruency analysis). Upon completion of the phase congruency analysis, a further object identifying step is performed based on the phase congruency analysis.

Method for 3D object identification and pose detection using phase congruency and fractal analysis

Method includes calculating a mean z coordinate value for points within the point cloud. An initial set of points is selected which have z coordinate values which deviate from the mean by at least an initial value. Thereafter, a triangulated irregular network (TIN) is constructed using the initial set of points. The method continues by determining if there is a significant point that exists among the points contained within an x, y extent of each triangle. If so, the TIN is updated to include the initial set of points and any significant points determined to exist within the triangles that form the TIN. Thereafter, the method continues by repeating the determining and the updating steps until there are no additional significant points found within the triangles.

Accuracy-based significant point derivation from dense 3D point clouds for terrain modeling

Method for improving the quality of a set of a three dimensional (3D) point cloud data representing a physical surface by detecting and filling null spaces (). The method includes analyzing () the data to identify the presence of a plurality of level 1 fractals (), each defined by a plurality of voxels () containing data points arranged in one of a plurality of three-dimensional patterns. The method also includes selectively filling () voxels in the 3D point cloud data with a first predetermined limited number of data points to increase a number of instances where level 2 fractals () can be used for representing the 3D point cloud data. Each level 2 fractal is defined as a common plurality of the level 1 fractals having a common three-dimensional pattern, where the common plurality of level 1 fractals are also arranged in accordance with the common three dimensional pattern.

Fractal method for detecting and filling data gaps within LiDAR data

A geospatial modeling system may include a geospatial model database configured to store a digital surface model (DSM) of a geographical area, and to store image data of the geographical area. The image data may have a spectral range indicative of a difference between buildings and vegetation. The geospatial modeling system may also include a processor cooperating with the geospatial model database to separate bare earth data from remaining building and vegetation data in the DSM to define a building and vegetation DSM. The processor may also register the image data with the building and vegetation DSM, and classify each point of the building and vegetation DSM as either building or vegetation based upon the spectral range of the image data.

Geospatial modeling system for classifying building and vegetation in a DSM and related methods

Method and system for creating a fused image from an image pair. The method includes obtaining () image data defining a first image of a panchromatic image type and a second image of a multi-spectral image type. The first image has a first spatial resolution and a first spectral resolution. The second image has a second spatial resolution which is lower than the first spatial resolution and a second spectral resolution higher than the first spectral resolution. The first image and the second image are fused () to initialize a fused image having the first spatial resolution and the second spectral resolution. A point spread function and a set of spectral weights are used to determine scaling factors for scaling () radiance value of pixels defining the fused image in accordance with radiance value of pixels forming the first image and the second image.

Panchromatic modulation of multispectral imagery

Method and system for creating a fused image from an image pair. The method includes obtaining () image data defining a first image of a panchromatic image type and a second image of a multi-spectral image type. The first image has a first spatial resolution and a first spectral resolution. The second image has a second spatial resolution which is lower than the first spatial resolution and a second spectral resolution higher than the first spectral resolution. The first image and the second image are fused () to initialize a fused image having the first spatial resolution and the second spectral resolution. A blurring function is used () to help form the fused image.

Structured smoothing for superresolution of multispectral imagery based on registered panchromatic image

Method and system for creating a fused image from an image pair comprising a high resolution panchromatic image and lower resolution multi-spectral image. The method includes obtaining image data () defining a first image of a panchromatic image type and a second image of a multi-spectral image type. The first image has a first spatial resolution and a first spectral resolution. The second image has a second spatial resolution which is lower than the first spatial resolution and a second spectral resolution higher than the first spectral resolution. The method also includes a step () of concurrently calculating a point-spread function for down-sampling the first image to the second spatial resolution, and a set of weights for down-sampling the second image to the first spectral resolution.

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Rockledge, FL, United States of America

Morteza Akbari