Water Budgeting via Spatial Structure Variance and Land Cover Classification
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Solution Overview
Problem
Conventional water management systems face inaccuracies in classifying land cover types due to reliance on single vegetation index-based classification methods, leading to incorrect water budgets and usage analysis, and lack a comprehensive method for predicting water usage at a parcel level.
Innovation Solution
The system incorporates a spatial structure variance generation component to enhance image data analysis, uses multiple classification components for accurate land cover classification through majority voting, and employs regression techniques for water use forecasting, integrating land cover data with historical usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single vegetation index-based classification method is used, then the classification process is simple and fast, but the classification accuracy of land cover types deteriorates
Solution Approach 1:
The patent combines multiple classification methods (vegetation index-based classification, spatial structure variance classification, and spectral classification) into a unified system that processes satellite imagery through multiple pathways and integrates their results through majority voting, thereby achieving both accuracy and efficiency
Solution Approach 2:
The patent introduces spatial structure variance as an intermediary parameter that bridges the gap between simple vegetation index methods and complex spectral analysis, providing additional structural information about land cover without requiring full spectral decomposition
2Measurement precision
If manual surveying is used to collect parcel data, then data accuracy can be verified, but the process becomes costly and time-consuming
Solution Approach 1:
The patent creates digital copies of parcel data from multiple satellite imagery sources and processing methods, allowing automated verification and cross-validation without requiring physical field surveys, thus maintaining accuracy while dramatically reducing time and cost
Solution Approach 2:
The system implements feedback loops where classification results are continuously refined by comparing multiple independent classification methods and using majority voting to correct errors, automatically verifying data accuracy without manual intervention
Data Source
AI summary
A device includes an image data receiving component, a vegetation index generation component, a spatial structure variance generation component, a classification component and a water budget component. The image data receiving component receives multiband image data of a geographic region. The vegetation index generation component generates a vegetation index based on the received multiband image data. The spatial structure variance generation component generates a spatial structure variance image band based on the received multiband image data. The classification component generates a land cover classification based on the received multiband image data, the vegetation index and the spatial structure variance image band. The water budget component generates a water budget of a portion of the geographic region based on the land cover classification.