Imaging Land Type Segmentation for Wide-Area Target Search
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Solution Overview
Problem
Conventional imaging systems struggle to efficiently search wide areas for rare targets due to the vastness of the area exceeding real-time assessment capabilities, necessitating improved image processing and analysis methods.
Innovation Solution
The method involves land type-based segmentation of images, grouping pixels by identical or hierarchical land types, using a lookup table to identify target likelihood, and prioritizing analysis and capture based on land types likely to contain targets, optimizing mission planning by focusing on regions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional imaging systems search wide areas for rare targets, then coverage area is improved, but processing time and resource utilization increase excessively
Solution Approach 1:
The imaging area is segmented into multiple sub-images based on land type classifications (e.g., water bodies, vegetation, urban areas). This segmentation allows the system to process and analyze only the relevant sub-images corresponding to areas where targets are most likely to be found, rather than processing the entire wide-area image, thereby reducing processing time while maintaining comprehensive coverage.
Solution Approach 2:
Different regions of the image are assigned different processing priorities based on their land type characteristics. Areas with land types having higher target likelihood scores receive prioritized processing and analysis resources, while areas with lower likelihood scores are processed less intensively or deferred, optimizing resource utilization across the entire coverage area.
2Area of stationary object
If conventional imaging systems search wide areas for rare targets, then coverage area is improved, but computational resources required increase excessively
Solution Approach 1:
The imaging area is segmented into multiple sub-images based on land type classifications (e.g., water bodies, vegetation, urban areas). This segmentation allows the system to process and analyze only the relevant sub-images corresponding to areas where targets are most likely to be found, rather than processing the entire wide-area image, thereby reducing processing time while maintaining comprehensive coverage.
Solution Approach 2:
Instead of applying full computational analysis to the entire imaging area, the system applies partial analysis only to sub-images corresponding to land types with higher target likelihood scores. This partial action approach reduces overall computational resource consumption while maintaining effective search capability in the most promising areas.
3Productivity
If land type-based segmentation is applied to prioritize search areas, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
Land type classification and target likelihood score assignment are performed as preliminary actions before the main image analysis process. By pre-segmenting the image into land type categories and pre-calculating likelihood scores based on historical data and target behavior patterns, the system reduces the complexity of the main processing task and improves overall efficiency.
Solution Approach 2:
Land type classification serves as an intermediary layer between the raw image data and the target search process. This intermediary classification system simplifies the complex task of searching wide areas by providing a structured framework for prioritizing and filtering potential search areas, making the overall system more manageable and efficient.
Data Source
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AI summary
An imaging system (100) comprises an imaging platform (102), a camera (104) operatively connected to the imaging platform, and a controller (106) operatively connected to control the imaging platform and the camera. The controller includes machine readable instructions configured to cause the controller to perform a method.