Curvature Wavelet DEM Generation for Terrain Feature Preservation
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Solution Overview
Problem
Existing DEM synthesis methods lack effective control of structural information, require complex terrain structure extraction, and lack concrete algorithms for extracting important terrain features as point elements, leading to incomplete or inefficient multi-scale DEM generation.
Innovation Solution
A method for multi-scale DEM generation based on curvature wavelet transform, involving wavelet basis function, decomposition, and reconstruction to extract terrain feature points using a feature information threshold, followed by cubic interpolation and resampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial/frequency filtering method is used for DEM synthesis, then processing efficiency is improved, but structural information control is lost and main terrain features are weakened
Solution Approach 1:
The patent introduces curvature as an intermediary parameter to bridge the original DEM data and the synthesis process. By calculating curvature values and using them as weights in the wavelet decomposition process, the method preserves structural information while maintaining processing efficiency. The curvature map serves as a mediator that guides which features should be preserved during the synthesis operation.
2Loss of information
If structure selection method is used for DEM synthesis, then terrain feature preservation is improved, but algorithm complexity increases and practical application becomes difficult
Solution Approach 1:
The patent extracts curvature information from the original DEM data as a separate component. By calculating the curvature map and using it to weight the wavelet coefficients, the method selectively preserves important terrain features while simplifying the overall algorithm structure. This extraction approach avoids the complexity of traditional structure selection methods while maintaining feature preservation.
3Measurement precision
If importance evaluation method is used for DEM synthesis, then key terrain representation is improved, but extraction algorithm complexity increases and concrete implementation is lacking
Solution Approach 1:
The patent changes the parameter space by using curvature values as importance indicators instead of requiring complex extraction algorithms. By transforming the DEM data into curvature space and using wavelet decomposition with curvature-based weighting, the method automatically identifies and preserves key terrain features through parameter transformation rather than complex feature extraction.
Data Source
AI summary
A multi-scale DEM generation method based on curvature wavelet transform is provided. The method includes following steps: obtaining a wavelet basis function and a wavelet decomposition layer number of the curvature wavelet transform, and a DEM related parameter feature information amount and an output resolution; an grid surface curvature of DEM data is acquired, and the grid surface curvature of the DEM data is decomposed and reconstructed based on the wavelet basis function and the wavelet decomposition layer number to obtain a reconstruction matrix; based on the reconstruction matrix, terrain feature points in the DEM data are extracted by taking a feature information amount as a threshold; and based on the terrain feature points in the DEM data, resampling is performed with the output resolution as resolution, so that DEM generation and synthesis are realized.