Automated Flood Mapping via SAR Data Processing
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Solution Overview
Problem
Current satellite-based inundation mapping during floods faces challenges in processing Synthetic Aperture Radar (SAR) data quickly into reliable flood maps, particularly due to the need for human intervention and the inability to automate supervised classification methods effectively.
Innovation Solution
A fully automated system, RAPID, which integrates a flood trigger system, SAR data query system, and a kernel algorithm that uses binary classification, morphological processing, multi-threshold compensation, and machine-learning correction to generate flood inundation maps in near real-time, reducing human interference and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised classification methods are used for SAR data processing, then accuracy is improved, but automation capability deteriorates due to required human intervention
Solution Approach 1:
The system performs self-service through automated unsupervised classification algorithms that automatically determine water presence in SAR imagery without requiring human operators to select training samples or adjust parameters. The algorithm independently processes the SAR data using change detection and thresholding methods to generate flood maps autonomously
Solution Approach 2:
The patent replaces the mechanical process of manual supervised classification with an automated computational system. Instead of human operators manually selecting training samples and adjusting classification parameters, the system uses automated image processing algorithms including change detection, histogram analysis, and threshold-based classification to achieve flood detection
2Measurement precision
If manual processing and editing is used for flood map generation, then accuracy is improved, but processing time increases significantly
Solution Approach 1:
The system performs preliminary automated processing steps including change detection between pre-flood and post-flood SAR images, automatic threshold determination, and initial flood area identification. This preliminary automated action prepares the data for minimal manual verification, significantly reducing the time required for manual processing while maintaining accuracy
Solution Approach 2:
The patent implements continuous automated processing that operates without interruption from manual intervention. The system continuously processes SAR imagery through a pipeline of automated algorithms including radiometric correction, change detection, classification, and map generation, enabling near-real-time flood mapping without the start-stop nature of manual processing
3Speed
If SAR data is processed quickly for near real-time flood maps, then response time is improved, but processing reliability may deteriorate due to rushed analysis
Solution Approach 1:
The patent segments the flood mapping process into distinct automated stages: SAR data reception and preprocessing, change detection between temporal images, classification of water versus non-water areas using histogram analysis, validation against threshold criteria, and final map generation. This segmentation allows each stage to be optimized for speed while maintaining reliability through systematic quality control at each step
Solution Approach 2:
The system incorporates automated feedback mechanisms where processing results from each stage are automatically validated against predefined criteria. Change detection results are feedback-checked for consistency, classification outputs are validated against threshold values, and the system automatically adjusts processing parameters based on data quality metrics, ensuring reliability even at high processing speeds
Data Source
AI summary
A system and method to generate flood inundation maps in near real time. The system includes a plurality of computer processing modules: a flood trigger system, a SAR data query system, and a RAPID kernel algorithm system, running in real time, to identify the potential flood zones, query SAR data, and finally compute the inundation maps, respectively. As disclosed herein, the RAPID kernel algorithm is extended to a fully automated flood mapping system that requires no human interference from the initial flood events discovery to the final flood map production.