Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks.

Area of Science:

• Artificial Intelligence
• Signal Processing
• Machine Learning

Background:

• High-resolution imagery classification is vital for applications like landmine and tumor detection.
• Large neural networks, including Convolutional Neural Networks (CNNs) and Wavelet Scattering Networks (WSNs), face memory constraints and limited adaptability.
• Existing methods struggle with adaptive and online learning scenarios.

Purpose of the Study:

• To quantitatively assess quantization schemes for WSNs in target classification.
• To evaluate the robustness of these quantized WSNs under low signal-to-noise ratio (SNR) conditions.
• To provide guidance for designing efficient, quantized neural network architectures.

Main Methods:

• Utilized X-band synthetic aperture radar (SAR) data for target classification tasks.
• Investigated various quantization schemes applied to WSNs.
• Conducted a detailed analysis of the trade-offs between quantization methods and classification performance.

Main Results:

• Quantization schemes were quantitatively studied on WSNs for SAR target classification.
• The robustness of quantized WSNs to low SNR levels was investigated.
• Trade-offs between quantization and classification performance were analyzed to maximize accuracy.

Conclusions:

• WSN-based quantization studies offer a benchmark for quantized neural network design.
• The findings provide important guidance for developing adaptive and online learning architectures.
• Optimized quantization strategies are crucial for efficient target classification in challenging environments.