Aerial Image Obstacle Mapping for Low-Altitude Height Detection
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Solution Overview
Problem
Current technologies are inadequate for comprehensively determining altitude obstacles across various geographical areas, which poses a risk to aircraft navigation at low or very low altitudes.
Innovation Solution
A method and device that utilize digital aerial images and advanced image processing techniques, including neural networks for segmentation and stereoscopic processing, to identify and consolidate altitude obstacles with heights greater than or equal to a predetermined minimum height, providing accurate location and height data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If commercial products are used to list altitude obstacles, then obstacle information is available, but the level of completeness is not satisfactory
Solution Approach 1:
The system uses multi-source data acquisition including satellite imagery, aerial photography, and ground-based surveys to create a universal obstacle database that covers diverse geographical areas and terrain types, making the solution applicable to any location rather than being limited to specific regions
Solution Approach 2:
The geographical area is divided into multiple zones or regions, with obstacle detection and classification performed separately for each zone using appropriate imaging sources and processing methods, then consolidated into a comprehensive database
2Extent of automation
If image processing is applied to digital aerial images, then obstacle identification is automated, but processing time and computational resources increase
Solution Approach 1:
Image preprocessing steps such as normalization, enhancement, and feature extraction are performed before main obstacle detection to reduce computational complexity of subsequent processing. Training data for neural networks is prepared in advance to speed up real-time or near-real-time detection
Solution Approach 2:
The system dynamically adjusts processing parameters, resolution levels, and detection sensitivity based on the specific characteristics of each image and the expected obstacle types in different geographical areas, optimizing processing speed while maintaining detection accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively automates the identification and consolidation of altitude obstacles, providing a comprehensive database that enhances aircraft navigation safety by accurately mapping obstacles across any geographical area.
Implementation Method 1
The first image processing includes a segmentation by the application of a neural network, previously trained to identify obstacles belonging to one of the categories of said plurality of categories of obstacles.
Implementation Method 2
The second image processing is a stereoscopic processing, comprising a processing in pairs of digital images of said set of digital aerial images, a pair of digital images including two digital images of the same portion of terrain acquired with a known spatial camera offset, said stereoscopic processing consisting of obtaining a three-dimensional reconstruction of said portion of terrain.
Implementation Method 3
The calculation is a function of a camera angle of a device for acquiring an digital aerial image comprising said identified obstacle or of a corresponding illumination angle from the sun and an estimate of the length of the shadow of said identified obstacle in said digital aerial image.
Data Source
AI summary
The invention relates to a method and a device for determining altitude obstacles. The method includes, for a given geographical area, obtaining (50) digital aerial images of portions of terrain of said geographical area from at least one digital aerial image source, forming a set of digital aerial images covering said geographical area. The method includes applying (54) a first image processing to the digital images of said set of images so as to obtain a first set of obstacles present in said geographical area and an associated first height estimate, then applying (56) a second image processing to the images of said set of digital images so as to obtain a second set of obstacles present in said geographical zone and a second associated height estimate, greater than or equal to said predetermined minimum height. Depending on the first and second height estimates, a consolidated set of altitude obstacles with a height greater than or equal to said predetermined minimum height, present in said geographical area, is obtained (66, 68) and attributes of said altitude obstacles are stored (70).