Satellite Image Annotation via Candidate Region and Standard Image Association
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Solution Overview
Problem
Existing image processing systems face challenges in accurately annotating objects in satellite images, particularly those generated by synthetic aperture radar, due to difficulty in determining the content, leading to inefficient annotation processes.
Innovation Solution
An image processing device that sets candidate regions in images, extracts standard images from annotated images, and generates annotation data by associating annotation target images with reference images captured at different times, improving annotation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic analysis methods are developed for satellite images, then productivity of annotation is improved, but measurement precision of object determination deteriorates
Solution Approach 1:
The patent introduces an intermediary system that automatically generates candidate region data from satellite images, which then serves as a foundation for human annotators to refine and verify. This intermediary automatic processing step handles the initial heavy lifting of identifying potential objects, while human annotators focus on verifying and correcting these candidates, thus maintaining both high productivity and high measurement precision.
Solution Approach 2:
The annotation process is segmented into distinct stages: automatic candidate region generation, candidate region verification, and final annotation confirmation. By dividing the workflow into these segments, the system can leverage automated methods for initial processing while reserving human expertise for critical verification steps, thereby resolving the contradiction between automation efficiency and determination accuracy.
2Measurement precision
If more image data is annotated to improve automatic analysis accuracy, then measurement precision is improved, but loss of time increases due to complicated annotation work
Solution Approach 1:
The system performs preliminary automatic generation of candidate regions before human annotation begins. By pre-processing the images to identify and mark potential objects of interest, the system reduces the time required for human annotators to perform their work, as they only need to verify and refine pre-identified candidates rather than searching through entire images from scratch.
Solution Approach 2:
The annotation system is designed to be partially self-service through automatic candidate region generation and management. The system automatically handles tasks such as image processing, candidate identification, and data organization, freeing human annotators to focus solely on the critical verification and confirmation tasks, thereby reducing overall annotation time while maintaining high precision.
3Productivity
If transfer reading system compares two images to determine object presence, then productivity is improved, but measurement precision deteriorates for difficult-to-determine objects
Solution Approach 1:
The system incorporates feedback mechanisms where human annotators review and correct automatic candidate regions, and their corrections feed back into improving the automatic generation algorithm. This feedback loop ensures that while automatic processing maintains high productivity, measurement precision is continuously improved through human verification and system learning from correction patterns.
Solution Approach 2:
For difficult-to-determine objects, the system introduces an intermediary verification step where human annotators specifically review automatic candidates. This intermediary human judgment layer compensates for the limitations of automatic comparison methods while maintaining overall productivity, as most objects can still be processed automatically with high confidence.
Data Source
AI summary
This image processing device is configured to comprise a region setting unit, a standard image extraction unit, a data generation unit, and an output unit. The region setting unit sets, in an annotation target image, a region in which an annotation target object can be present as a candidate region. The standard image extraction unit extracts, from an image on which annotation has been completed, a standard image that is an image in which an object identical to the target object is captured. The data generation unit generates, as annotation data, data in which the annotation target image, a reference image which is captured of a region including the candidate region at a time different from the time when the annotation target image is captured, and the standard image are associated with each other. The output unit outputs the generated annotation data generated.