Remote Sensing Image Processing via Parameter Space Transformation
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Solution Overview
Problem
High-resolution images in remote sensing applications require significant resources for processing, leading to increased complexity and inefficiency, as existing methods do not effectively manage the processing of large amounts of data from these images.
Innovation Solution
A piecewise approach is implemented using a system with a processor and memory configured to transform image points into a parameter space, identify geometry features, and generate composite images by augmenting pixels based on intersection points, allowing for reduced resource consumption through sequential or parallel processing of high-resolution image portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are processed using existing methods, then detailed geometry features can be detected, but resource consumption increases significantly and processing efficiency decreases
Solution Approach 1:
The patent divides the high-resolution image into multiple overlapping portions or tiles, each processed independently to identify geometry features. This segmentation allows parallel processing of different regions, significantly improving processing efficiency while maintaining detection accuracy through the overlapping regions that ensure continuous coverage.
Solution Approach 2:
The patent transforms image points from 2D image space coordinates to parameter space coordinates using coordinate transformation. This dimensional change enables the application of efficient parameter space algorithms for geometry feature detection, reducing computational complexity while preserving geometric relationships.
2Loss of information
If high-resolution images are processed using existing methods, then comprehensive visual analytics can be performed, but resource consumption and system complexity increase
Solution Approach 1:
The system processes the image in segmented portions rather than as a complete high-resolution image, reducing memory requirements and computational complexity. Each portion is processed independently to extract geometry features, and results are integrated to provide comprehensive visual analytics coverage of the entire image.
Solution Approach 2:
The patent processes only necessary portions of the image at any given time rather than the entire high-resolution image simultaneously. This partial action approach reduces resource consumption while excessive sampling through overlapping portions ensures no information is lost at boundaries.
Data Source
AI summary
Methods and systems for generating a composite image in remote sensing applications are described. In an example, a device can receive an image having a plurality of points specified in an image space. The device can extract a portion of the image and transform points among the extracted portion from the image space to a parameter space defined by a distance parameter and an orientation parameter. The device can identify a set of intersection points in the parameter space that indicate at least one occurrence of a geometry feature in the extracted portion of the image. The device can augment the portion of the image with a plurality of new pixels based on the identified set of intersection points. The device can generate a composite image using the augmented image, where the composite image can include a plurality of augmented images corresponding to other portions of the image.