Automated Feature Extraction and Labeling for ATR Systems
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Solution Overview
Problem
Conventional ATR systems face challenges in providing explainable target decisions and require lengthy model training times, along with costly and labor-intensive manual labeling processes, especially for rare targets and complex imagery like ISAR.
Innovation Solution
An ATR system is developed for automated feature extraction and labeling, using an object feature map generation method that includes an object knowledge-base and ensemble classifiers to automatically identify and classify object features, reducing the need for manual labor and speeding up the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated feature extraction and labeling systems are implemented, then productivity and training speed are improved, but device complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-defining feature templates and object definitions before actual feature extraction. The feature template library stores pre-configured feature types, extraction methods, and labeling rules, allowing the system to automatically process new images without requiring complex real-time decision-making about feature extraction strategies.
Solution Approach 2:
The patent introduces an intermediary layer consisting of feature templates and object definitions that mediate between raw image data and classification results. These templates act as standardized intermediaries that simplify the interaction between different system components, reducing overall system complexity while maintaining high productivity.
2Measurement precision
If manual labeling processes are used for training data, then measurement precision and label accuracy are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system implements self-service by enabling automated feature extraction and labeling using pre-defined templates and object definitions. The feature extraction module automatically identifies and labels features in images without human intervention, and the system can self-train by generating labeled datasets from unlabeled images using its own extraction capabilities.
Solution Approach 2:
The patent applies parameter changes by adjusting template matching thresholds, feature extraction parameters, and confidence scores to optimize both accuracy and speed. The system dynamically modifies extraction parameters based on image characteristics, achieving high labeling accuracy while significantly reducing processing time compared to manual methods.
3Measurement precision
If more training data is collected for rare targets, then classification accuracy is improved, but loss of time and resources increase
Solution Approach 1:
The system uses copying by generating synthetic training data through template-based feature extraction and augmentation. Instead of collecting extensive real-world examples of rare targets, the system creates multiple variations of limited samples by applying different transformations, noise patterns, and contextual scenarios, effectively multiplying the value of scarce training data.
Solution Approach 2:
The patent implements preliminary action by pre-defining feature templates and object definitions that capture essential characteristics of rare targets. These pre-configured templates allow the system to identify and classify rare targets accurately even with limited training examples, as the templates encode domain knowledge about target features that would otherwise require extensive training data to learn.
4Measurement precision
If complex classification models are used, then classification accuracy is improved, but loss of time in model training increases
Solution Approach 1:
The patent applies segmentation by dividing the classification task into multiple stages: feature extraction, feature selection, and classification. Each stage uses simplified models optimized for its specific function, rather than employing a single complex end-to-end model. This segmentation reduces training time for each individual model while maintaining or improving overall classification accuracy through the coordinated pipeline.
Solution Approach 2:
The system extracts and separates feature extraction and selection as independent preprocessing stages before classification. By taking out these functions and handling them separately with dedicated modules, the patent reduces the complexity and training time of the final classification model, as it receives pre-processed, highly relevant features rather than raw image data.
Data Source
AI summary
Devices, systems, and methods for machine learning model generation. A method can include generating image chips of an image. The image chips can each provide a view of a different extent of an object in the image. Based on an object definition that indicates respective features of the object and a location of the respective features along a length of the object, it can be determined whether any of the image chips include any of the respective features. Each image chip of the image chips can be labeled to include an indication of any of the features included in the image chip resulting in labelled image chips. The method can include training an ensemble classifier based on the labelled image chips resulting in a trained ensemble classifier.