Remote Container Volume Analysis Using Idealized Image Templates
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Solution Overview
Problem
Current aerial imaging systems, especially low spatial resolution satellites, provide unclear images of remote objects like containers or tanks, making them difficult to identify and measure accurately, and are costly to maintain and operate, leading to a slowdown in the introduction of new satellite imagery services.
Innovation Solution
A remote container analysis system that uses machine learning models and image processing techniques to extract feature vectors from low-resolution images, generate idealized image templates, and perform dot product matching to determine the filled volume of cylindrical containers or tanks, even when the imaging device's angles are inaccurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive high spatial resolution imaging satellites are used, then image quality and object identification accuracy are improved, but operational costs and device complexity increase significantly
Solution Approach 1:
The patent creates idealized template images that represent typical container appearances from various angles and lighting conditions. These templates serve as reference copies that can be matched against actual satellite imagery, eliminating the need for expensive high-resolution satellites while maintaining identification accuracy.
Solution Approach 2:
The system varies parameters such as lighting conditions, viewing angles, and container states when generating idealized templates. This creates a comprehensive set of reference images that account for different operational conditions, enabling accurate identification without requiring high-resolution imaging.
2Device complexity
If cheaper low spatial resolution imaging satellites are used, then operational costs are reduced, but image clarity and object identifiability deteriorate
Solution Approach 1:
The patent creates idealized template images that represent typical container appearances from various angles and lighting conditions. These templates serve as reference copies that can be matched against actual satellite imagery, eliminating the need for expensive high-resolution satellites while maintaining identification accuracy.
Solution Approach 2:
The system introduces idealized templates as an intermediary between the low-resolution satellite imagery and the object identification process. These templates act as a bridge that enables accurate container identification even when the input images are of low quality.
3Difficulty of detecting and measuring
If sophisticated equipment and ground facilities are deployed, then detection capability is improved, but operational costs and maintenance requirements increase
Solution Approach 1:
The system uses automated image processing algorithms that independently generate idealized templates and perform matching without requiring sophisticated ground facilities or manual intervention. This self-service approach reduces operational complexity and maintenance requirements.
Solution Approach 2:
The patent replaces complex mechanical and human-operated ground facilities with computational algorithms that automatically process satellite imagery. This substitution of mechanical systems with information processing reduces operational costs and maintenance needs.
4Measurement precision
If high resolution aerial images are obtained, then object identification accuracy is improved, but resource consumption and launch costs increase
Solution Approach 1:
The patent creates idealized template images that represent typical container appearances from various angles and lighting conditions. These templates serve as reference copies that can be matched against actual satellite imagery, eliminating the need for expensive high-resolution satellites while maintaining identification accuracy.
Data Source
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AI summary
Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.