Multispectral Infrastructure Classification for Green-Blue-Gray Mapping
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Solution Overview
Problem
Existing methods for classifying urban infrastructure in cities have low accuracy and efficiency, particularly in distinguishing between green-blue-gray infrastructure types, which affect rainwater-utilization planning.
Innovation Solution
A method involving multispectral photo processing, including two-dimensional rebuilding, mesh image generation, and spectral similarity-based partitioning, combined with accuracy evaluation, to classify green-blue-gray infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used for classifying urban infrastructure, then the classification process is simple, but the accuracy and efficiency are low
Solution Approach 1:
The patent segments the classification process into distinct modules: data acquisition module (collecting multispectral images, vector data, terrain data), feature extraction module (extracting spectral, spatial, and elevation features), classification module (using machine learning algorithms), and validation module (assessing classification accuracy). This segmentation allows each module to be optimized independently, improving overall classification accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent changes multiple parameters to improve classification accuracy: using multispectral imagery with multiple wavelength bands instead of single-band images, incorporating additional data types (vector data, terrain data), extracting multiple feature types (spectral, spatial, elevation features), and applying advanced machine learning algorithms. These parameter changes collectively enhance the discriminative power for distinguishing green-blue-gray infrastructure.
2Productivity
If existing methods are used for classifying urban infrastructure, then the processing approach is simple, but the efficiency is low
Solution Approach 1:
The patent performs preliminary actions by pre-processing the input data before classification: multispectral images undergo radiometric and geometric correction, vector data is pre-processed to extract relevant features, and terrain data is processed to generate elevation features. This preliminary processing prepares the data in an optimal format for classification, improving efficiency by avoiding redundant computations during the main classification process.
Solution Approach 2:
The patent replaces traditional manual or rule-based classification methods with machine learning-based automated classification. The system uses training samples to train classification models that automatically learn the characteristics of different infrastructure types, substituting mechanical/manual classification processes with intelligent algorithms that improve efficiency and consistency.
3Measurement precision
If multispectral photo processing with two-dimensional rebuilding and mesh image generation is performed, then the classification accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent transitions from two-dimensional image data to three-dimensional classification by incorporating elevation information from terrain data. The system generates a digital surface model and extracts elevation features, adding the vertical dimension to the classification process. This dimensional enhancement improves the ability to distinguish infrastructure types that have similar spectral characteristics but different spatial configurations.
Solution Approach 2:
The patent creates a composite data structure that integrates multiple data types: multispectral image data, vector data, and terrain data. By fusing these different data sources and their corresponding features (spectral, spatial, and elevation features), the system creates a comprehensive feature set that enhances classification accuracy beyond what any single data source could provide alone.
Data Source
AI summary
A method, device, system, and medium for classifying green-blue-gray infrastructure. The method includes obtaining a multispectral photo corresponding to a target area, and obtaining an image set of a target and a color orthophoto, based on the multispectral photo; obtaining a sample file based on the color orthophoto, and obtaining a classification result of the green-blue-gray infrastructure corresponding to the target area based on the image set of the target and the sample file. The embodiments can obtain the image set of the target and the color orthophoto based on the multispectral photo corresponding to the target area and combine the sample file obtained from the color orthophoto with the image set of the target to obtain the classification result of the green-blue-gray infrastructure corresponding to the target area, thereby improving the accuracy and efficiency for classifying the green-blue-gray infrastructure.