Neural Network Map Generation for Image Alignment
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Solution Overview
Problem
Existing methods for aligning aerial and satellite images with maps require significant manual effort and are prone to inaccuracies due to human error, especially when dealing with incorrect map data or distortions, which complicates the overlay of image and map data.
Innovation Solution
The use of cyclical generative adversarial networks (GANs) in deep learning models to automatically generate accurate maps and register images to maps, allowing for the alignment of image elements with map data without the need for labeled training data, by iteratively refining generator and discriminator models using composite loss measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to align images with maps, then flexibility and adaptability are maintained, but accuracy deteriorates due to human error and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical alignment methods with an automated deep learning system using neural networks. The system automatically processes images and maps to generate accurate alignments without human intervention, eliminating human error while maintaining high precision through trained models that learn optimal alignment parameters from data.
Solution Approach 2:
The neural network system performs self-service by automatically aligning images with maps using learned patterns and features. The system independently processes the alignment task through automated feature detection, parameter optimization, and registration without requiring manual intervention, thereby achieving both high accuracy and efficiency.
2Measurement precision
If automated deep learning methods are used to align images with maps, then accuracy and productivity are improved, but device complexity increases due to neural network systems
Solution Approach 1:
The patent segments the complex alignment task into distinct processing stages handled by specialized neural network components. The system divides image and map processing into separate operations including feature detection, parameter estimation, and registration, allowing each component to be optimized independently while reducing overall system complexity through modular architecture.
3Reliability
If traditional image processing methods are used, then system complexity remains low, but manufacturing precision and reliability deteriorate due to inability to handle distorted images and incorrect map data
Solution Approach 1:
The patent employs parameter changes by using neural networks to dynamically adjust alignment parameters based on learned patterns from training data. The system transforms fixed parameter approaches into adaptive parameter selection, allowing the model to optimize transformation parameters (translation, rotation, scaling) based on the specific characteristics of each image-map pair, thereby handling distortions and inaccuracies reliably.
Data Source
AI summary
Techniques for image processing and transformation are provided. A plurality of images and a plurality of maps are received, and a system of neural networks is trained based on the plurality of images and the plurality of maps. A first image is received, and a first map is generated by processing the first image using the system of neural networks.