Multi-pass Object Classification System for Remote Sensing
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Solution Overview
Problem
Existing remote sensing technologies are limited in their ability to effectively classify objects using data from multiple passes or views, leading to suboptimal performance in detecting and identifying targets, especially in environments like the sea floor where navigation errors and feature misalignment can occur.
Innovation Solution
A multi-pass classification system that combines features from current and previous observations to improve classification accuracy, using a single-pass classifier for new detections and a multi-pass classifier when features are close to existing database entries, with a fused multi-pass feature determiner to compute and update features for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-pass classifier is used for all detections, then the system complexity is low and processing is fast, but the classification accuracy deteriorates when navigation errors or feature misalignment occur
Solution Approach 1:
The classification system is segmented into two distinct pathways: a single-pass classifier for new detections and a multi-pass classifier for detections close to existing database entries. This segmentation allows the system to apply different levels of complexity appropriately, improving accuracy when needed while maintaining simplicity for routine cases.
Solution Approach 2:
The system performs preliminary action by checking whether a detection is close to an existing database entry before committing to a classification approach. This preliminary check enables the system to prepare the appropriate classification pathway in advance, avoiding unnecessary complexity while ensuring accuracy is applied when beneficial.
2Measurement precision
If data from multiple passes is integrated for all detections, then the classification accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by using multi-pass classification only for detections that are close to existing database entries, rather than applying it universally. This selective approach integrates data from multiple passes only when necessary, improving accuracy for relevant cases while avoiding unnecessary processing time for routine detections.
Solution Approach 2:
Different classification approaches are applied to different regions of the detection space: single-pass classification for novel detections and multi-pass classification for detections near known entries. This local quality approach ensures that computational resources are concentrated where they provide the most value, balancing accuracy improvement with processing efficiency.
3Reliability
If the system uses a multi-pass classifier for all potential targets, then the false alert rate decreases, but the fuel consumption and operational costs increase
Solution Approach 1:
The system segments the detection population into two groups: those requiring multi-pass classification to reduce false alerts and those that can be handled by single-pass classification. This segmentation ensures that the reliability improvements from multi-pass processing are achieved only where necessary, minimizing the operational costs and fuel consumption associated with extended processing.
Solution Approach 2:
The system performs a preliminary assessment to determine whether a detection warrants multi-pass classification based on its proximity to existing database entries. This preliminary action prevents unnecessary multi-pass processing for clear-cut cases, thereby reducing fuel consumption and operational costs while maintaining low false alert rates for detections that truly benefit from enhanced processing.
Data Source
AI summary
Classification of a potential target is accomplished by receiving image information, detecting a potential target within the image information and determining a plurality of features forming a feature set associated with the potential target. The location of the potential target is compared with a detection database to determine if it is close to an element in the detection database. If not, a single-pass classifier receives a potential target's feature set, classifies the potential target, and transmits the location, feature set and classification to the detection database. If it is close, a fused multi-pass feature determiner determines fused multi-pass features of the potential target and a multi-pass classifier receives the potential target's feature set and fused multi-pass features, classifies the potential target, and transmits its location, feature set, fused multi-pass features and classification to the detection database.