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Statistically Principled Deep Learning for SAR Image Segmentation

AAAI 2024 · Detection and segmentation › Open-set and few-shot detection · approach: Interactive and referring segmentation · 1 citations (OpenAlex)

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Proposes statistically grounded deep learning for SAR segmentation: G0-distribution synthetic data with Perlin-noise maps, a U-Net variant using 1x1 convolutions and average pooling to capture moments, and stochastic distance losses.

Problem

SAR image segmentation suffers from scarce labeled data, and deep learning and statistical SAR methods are typically used in isolation.

Approach

Combine known SAR statistics with deep learning: generate labeled synthetic SAR from the Generalized Gamma (G0) distribution, replace U-Net convolutions with 1x1 convolutions and average pooling to compute moment-like features, and add stochastic distance losses between region distributions.

Key results

• Standard U-Net trained on synthetic data reaches an average validation IOU of 82.2% with 1.34 ms average inference time.
• The synthetic-trained U-Net discerns regions of a real SAR oil-spill image in a general sense but yields coarse granularity, motivating stochastic distance losses.
• The proposed modified architecture and stochastic distance losses are described but not evaluated in the reported results.

Limitations

• Proposed architecture and stochastic distance losses are not implemented or evaluated in the reported results.
• Evaluation against traditional methods is not reported; only a standard U-Net baseline is described.
• Real-data test is qualitative on a single oil-spill image.

Experimental setup

Datasets: synthetic G0-distribution SAR data generated from Perlin-noise segmentation maps real SAR oil-spill image (Rodrigues et al.)

Benchmarks: synthetic validation set real SAR oil spill image (qualitative)

Metrics: intersection over union inference time

Baselines: standard U-Net traditional statistical methods

Builds on: U-Net stochastic distances

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