Cloud Feature Detection Using Image Moments for Aircraft Navigation
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Solution Overview
Problem
Detecting and ranging cloud features automatically without human input is challenging due to cloud motion and small triangulation baseline, complicating navigation for aircraft, especially in autonomous control systems.
Innovation Solution
A method and apparatus that classify image segments using luminance values to determine geometric representations, such as ellipses, of cloud features, allowing for distance calculation and collision time determination, enabling autonomous aircraft navigation by processing image data from cameras mounted on aircraft.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic cloud feature detection is implemented without human input, then autonomous navigation capability is improved, but detection reliability deteriorates due to cloud motion and small triangulation baseline
Solution Approach 1:
The system dynamically adapts to cloud motion by continuously tracking cloud features across multiple image frames and adjusting the geometric representation accordingly. The moment-based geometric representation evolves with cloud movement, maintaining accurate distance calculations despite the dynamic nature of cloud features and eliminating the need for large triangulation baselines.
2Measurement precision
If geometric representation methods are used to determine cloud distance, then ranging capability is improved, but system complexity increases due to moment calculation and geometric modeling
Solution Approach 1:
The patent replaces complex mechanical triangulation systems with a computational approach based on image moments and geometric representations. Instead of requiring physical baseline measurements and complex optical setups, the system uses mathematical moments (M00, M10, M01, etc.) to derive cloud distance, significantly reducing hardware complexity while maintaining measurement precision.
3Measurement precision
If image segment classification is used to identify cloud features, then detection accuracy is improved, but processing time increases due to luminance analysis and segment classification
Solution Approach 1:
The system divides the image into segments and classifies each segment based on luminance characteristics. By processing the image in segments rather than analyzing the entire image uniformly, the system achieves accurate cloud feature detection while reducing overall processing time. The segmentation allows parallel processing and focuses computational resources only on relevant cloud regions.
Data Source
AI summary
Disclosed is a method and apparatus for detecting and ranging cloud features. The method comprises: obtaining image data (e.g. using a camera (200); classifying, as a cloud feature, an image segment (502-508) of the image data; determining a plurality of moments of the image segment (502-508); using the determined plurality of moments, determining a geometric representation of that image segment (502-508); and, using the geometric representation, determining a distance between the cloud feature represented by that image segment (502-508) and an entity that obtained the image data.