Automatic generation of built-up layers from high resolution satellite image data
First Claim
1. A method of extracting built-up structures from satellite imagery data, comprising:
- organizing, using a processor, pixels of an input satellite image into a plurality of components of a first hierarchical data structure, wherein the input image is associated with a geographic area, and wherein each of the components is characterized by a vector of feature elements;
constructing, using the processor, a second hierarchical data structure that includes a plurality of hierarchically-arranged nodes, wherein each of the feature elements depends from at least one of the plurality of nodes, and wherein the constructing includes;
disposing the feature elements of the vectors of the components of the first hierarchical data structure into a feature space; and
recursively partitioning the feature elements in the feature space to create the plurality of hierarchically-arranged nodes of the second hierarchical data structure;
deriving training components from the plurality of components of the first hierarchical data structure that indicate built-up and non-built-up structures in the input image using a first reference image data set that is associated with the geographic area;
training, with the processor, the second hierarchical data structure with the vector of feature elements of each of the training components for detecting built-up structures;
classifying, with the processor and using the trained second hierarchical data structure, the plurality of components of the first hierarchical data structure as identifying built-up structures or non-built-up structures; and
mapping components of the plurality of components that identify built-up structures as classified during the classifying step into a resultant image that is associated with the geographic area.
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Abstract
A system for automatically extracting interesting structures or areas (e.g., built-up structures such as buildings, tents, etc.) from HR/VHR satellite imagery data using corresponding LR satellite imagery data. The system breaks down HR/VHR input satellite images into a plurality of components (e.g., groups of pixels), organizes the components into a first hierarchical data structure (e.g., a Max-Tree), generates a second hierarchical data structure (e.g., a KD-Tree) from feature elements (e.g., spectral and shape characteristics) of the components, uses LR satellite imagery data to categorize components as being of interest or not, uses the feature elements of the categorized components to train the second data structure to be able to classify all components of the first data structure as being of interest or not, classifies the components of the first data structure with the trained second data structure, and then maps components classified as being of interest into a resultant image.
24 Citations
20 Claims
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1. A method of extracting built-up structures from satellite imagery data, comprising:
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organizing, using a processor, pixels of an input satellite image into a plurality of components of a first hierarchical data structure, wherein the input image is associated with a geographic area, and wherein each of the components is characterized by a vector of feature elements; constructing, using the processor, a second hierarchical data structure that includes a plurality of hierarchically-arranged nodes, wherein each of the feature elements depends from at least one of the plurality of nodes, and wherein the constructing includes; disposing the feature elements of the vectors of the components of the first hierarchical data structure into a feature space; and recursively partitioning the feature elements in the feature space to create the plurality of hierarchically-arranged nodes of the second hierarchical data structure; deriving training components from the plurality of components of the first hierarchical data structure that indicate built-up and non-built-up structures in the input image using a first reference image data set that is associated with the geographic area; training, with the processor, the second hierarchical data structure with the vector of feature elements of each of the training components for detecting built-up structures; classifying, with the processor and using the trained second hierarchical data structure, the plurality of components of the first hierarchical data structure as identifying built-up structures or non-built-up structures; and mapping components of the plurality of components that identify built-up structures as classified during the classifying step into a resultant image that is associated with the geographic area. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of extracting built-up structures from satellite imagery data, comprising:
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organizing, using a processor, an input satellite image into a plurality of components of a first hierarchical data structure, wherein the input image is associated with a geographic area and has a first resolution, and wherein each of the components is characterized by a plurality of feature elements; constructing, using the processor, a second hierarchical data structure that includes a plurality of hierarchically-arranged nodes, wherein each of the feature elements depends from at least one of the plurality of nodes, and wherein the constructing includes; disposing the feature elements of the vectors of the components of the first hierarchical data structure into a feature space; and recursively partitioning the feature elements in the feature space to create the plurality of hierarchically-arranged nodes of the second hierarchical data structure; and deriving training components from the plurality of components of the first hierarchical data structure that indicate built-up and non-built-up structures in the input image using a first reference image data set that is associated with the geographic area and has a second resolution lower than the first resolution. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A system for extracting structures of interest from optical images, comprising:
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a construction engine, executable by a processor, that organizes the pixels of an input image associated with a geographic area and having a first resolution into a plurality of components, generates a first hierarchical data structure from the plurality of components that includes a plurality of k-dimensional feature elements of each of the components, and creates a second hierarchical data structure that includes a plurality of hierarchically-arranged nodes, wherein each of the feature elements depends from at least one of the plurality of nodes, and wherein the construction engine creates the second hierarchical data structure by disposing the feature elements of the vectors of the components of the first hierarchical data structure into a feature space and recursively partitioning the feature elements in the feature space to create the plurality of hierarchically-arranged nodes of the second hierarchical data structure; and a training engine that uses feature elements of a first portion of the plurality of components of the first hierarchical data structure to train the second hierarchical data structure to detect components of the plurality of components in the first hierarchical data structure that correspond to structures of interest in the input image. - View Dependent Claims (18, 19, 20)
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Specification