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Compression of infrared imagery sequences containing a slow-moving point target, part II
Revital Huber-Shalem1, Ofer Hadar, Stanley R Rotman
1Department of Communication Systems Engineering, Ben Gurion University of the Negev, Be'er Sheva, Israel. revital.huber@gmail.com
Applied Optics
|March 13, 2013
Summary
This study introduces a novel compression method for infrared imagery, enhancing point target detection. The method improves detection accuracy and reduces data transmission needs for slow-moving targets.
Area of Science:
- Image processing
- Signal processing
- Remote sensing
Background:
- Infrared (IR) imagery is crucial for detecting small, slow-moving targets like distant aircraft.
- Transmitting and storing IR sequences is resource-intensive, necessitating efficient compression.
- Previous work focused on temporal compression methods like DCT quantization and parabola fitting.
Purpose of the Study:
- To develop and evaluate a novel compression method for IR imagery that preserves point target detection capabilities.
- To extend existing temporal compression techniques by incorporating spatial compression and bit encoding.
- To introduce an automatic detection algorithm for improved target localization from compressed data.
Main Methods:
- Extension of Discrete Cosine Transform (DCT) quantization with spatial compression and bit encoding.
- Evaluation using a signal-to-noise ratio (SNR)-based measure for point target detection.
- Development of an automatic detection algorithm for target location extraction from SNR scores.
Main Results:
- The proposed compression method outperforms the H.264 standard in preserving point target detection capabilities.
- The automatic detection algorithm achieves a probability of detection and false alarm rate comparable to original sequences.
- Modified noise level calculation enables target detection across diverse background types.
Conclusions:
- The combined temporal and spatial compression method is effective for IR imagery, maintaining critical detection performance.
- The developed automatic detection algorithm provides robust target localization from compressed IR data.
- This approach offers significant advantages in data handling and target detection for remote sensing applications.
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