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Improving SAR Target Recognition Performance Using Multiple Preprocessing Techniques.

Qinmin Ma¹

¹School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China.

Computational Intelligence and Neuroscience

|August 16, 2021

Summary

Combining synthetic aperture radar (SAR) image preprocessing techniques like cropping, segmentation, and enhancement significantly boosts target recognition accuracy. This approach effectively suppresses background noise and highlights target features for better performance.

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Area of Science:

Remote Sensing
Computer Vision
Signal Processing

Background:

Synthetic Aperture Radar (SAR) imaging is crucial for surveillance and reconnaissance.
Accurate target recognition in SAR imagery is challenging due to noise and complex backgrounds.
Preprocessing is vital for enhancing SAR image quality and improving recognition rates.

Purpose of the Study:

To investigate the impact of various SAR image preprocessing techniques on target recognition performance.
To develop an optimized preprocessing pipeline for improved SAR-based target identification.
To evaluate the effectiveness of combined preprocessing methods for SAR target recognition.

Main Methods:

Image preprocessing techniques including cropping, segmentation, and enhancement were applied to SAR images.
Feature extraction was performed using the monogenic signal.
Classification was achieved using sparse representation-based classification (SRC).
Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.

Main Results:

The combination of image cropping, target segmentation, and image enhancement significantly improved SAR target recognition performance.
Preprocessing effectively suppressed background redundancy and enhanced target characteristics.
The proposed method demonstrated superior performance on the MSTAR dataset compared to baseline approaches.

Conclusions:

A composite approach to SAR image preprocessing is highly effective for enhancing target recognition.
The integration of specific preprocessing steps, feature extraction, and classification algorithms leads to robust target identification.
The findings provide a valuable framework for improving automated target recognition systems using SAR data.