Multispectral Surface Contact Detection for Maritime Assets
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Solution Overview
Problem
Existing methods for detecting and tracking surface contacts, such as small boats and floating mines, with low radar cross section from maritime assets are labor-intensive and unreliable due to environmental factors like glint and waves, leading to reduced effectiveness in threat detection and classification.
Innovation Solution
A processor-implemented method and system using multispectral imaging sensors to detect anomalies in pixel variance, analyze object motion relative to surface waves, and classify objects based on size, velocity, direction, and texture, with compensation for sensor and vessel motion, and passive ranging techniques to estimate range and velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual surveillance techniques are used to detect surface contacts, then human operators can identify potential threats, but labor intensity increases and reliability decreases due to fatigue and environmental effects
Solution Approach 1:
The patent replaces manual mechanical surveillance with an automated optical detection system. The system uses electro-optic sensors, infrared sensors, and image processing algorithms to automatically detect, track, and classify surface contacts, eliminating the need for human operators to manually scan and identify threats, thereby improving reliability while reducing operational effort
Solution Approach 2:
The system performs self-service by automatically detecting anomalies in image data, tracking object motion, compensating for platform motion, and classifying threats without human intervention. The automated processing pipeline includes anomaly detection circuits, motion analysis circuits, and classification circuits that work autonomously to identify and characterize potential threats
2Difficulty of detecting and measuring
If electro-optic or infrared sensors are used to detect surface contacts, then detection capability is provided, but detection reliability is reduced by environmental effects such as glint, waves, and white caps
Solution Approach 1:
The system changes the parameter of spectral analysis by using multispectral imaging sensors that capture data across multiple wavelength bands. This allows the system to distinguish between different surface features (such as separating glint from actual objects) by analyzing how different materials reflect or emit energy across various spectral bands, thereby improving detection reliability in challenging environmental conditions
Solution Approach 2:
The system converts the harmful effect of environmental factors into a beneficial detection mechanism. By analyzing the unique spectral signatures and motion patterns of environmental features like waves and white caps, the system learns to distinguish them from actual threats. The anomaly detection algorithm uses the statistical properties of environmental clutter to identify deviations that indicate real objects, effectively turning environmental noise into a reference framework for detection
3Area of stationary object
If numerous watch standers are deployed to manually survey the ocean surface, then coverage area increases, but productivity decreases due to labor intensity and fatigue
Solution Approach 1:
The system provides universal surveillance capability by using a single automated platform that can simultaneously monitor large ocean areas across multiple spectral bands and detect various types of threats (small boats, floating mines, other hazards). The system replaces multiple specialized human operators with one multi-functional automated system that maintains continuous surveillance without fatigue, thereby improving both coverage area and detection efficiency
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
This approach enhances the reliability and efficiency of detecting and tracking surface contacts, reducing manual effort and improving threat detection and classification accuracy, particularly in littoral zones, by filtering environmental effects and using multispectral data for improved object classification.
Implementation Method 1
receiving image data from one or more multispectral imaging sensors associated with a maritime asset
Data Source
AI summary
Techniques are provided for detection, classification and tracking of surface contacts based on multispectral image data from sensors on maritime assets. An example system may include an anomaly detection circuit configured to detect an object based on anomalies in the variance of pixels of water surface image data. The system may also include an object motion tracking circuit configured to analyze motion of the object relative to water surface waves. The analysis may compensate for motion of the asset and sensors. The system may further include an object classification circuit configured to classify the object as an object of interest, based on the analyzed motion of the object, and as a threat, further based on size estimation, edge detection, surface texture analysis, and volume analysis. The range from the asset to the object may be estimated based on the relation of the object to a detected or estimated horizon line.