Deep Learning Meets SAR (2006.10027v2)

Abstract: Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

Deep Learning Meets SAR: A Comprehensive Overview

The paper "Deep Learning Meets SAR" provides a thorough review of the intersection between deep learning methodologies and Synthetic Aperture Radar (SAR) applications. Over recent decades, SAR technology has been foundational in remote sensing, providing valuable data for applications ranging from topographic mapping to disaster monitoring. However, unlike the advancements seen in optical remote sensing, deep learning's potential in the SAR domain remains largely untapped.

Key Contributions and Challenges

The authors begin by highlighting existing deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), exploring their adaption to SAR data processing. Significant attention is given to deep learning's ability to automate and enhance the feature extraction process for SAR images, a process historically constrained by manually engineered features tailored for specific use-cases.

A core challenge discussed is the nature of SAR data itself, which significantly differs from optical data due to its complex-valued nature and multiplicative noise characteristics (speckle). SAR imaging also exhibits high dynamic range, distinct imaging geometry, and unique statistical properties, all of which necessitate specialized preprocessing steps or adaptations of conventional deep learning models to effectively process SAR imagery.

Applications in SAR

The paper is organized into several key areas where deep learning has been applied to SAR data:

1. Terrain Surface Classification: Deep learning models, especially those employing CNNs, have shown promising results in automating the classification of polarimetric SAR (PolSAR) data, overcoming issues related to human-designed feature extraction by learning comprehensive feature representations directly from raw data.
2. Object Detection: Borrowing from techniques developed for optical imagery, deep learning has been successfully applied to SAR object detection tasks, particularly in identifying military vehicles and ships. These tasks have benefited from the robust feature extraction capabilities of deep learning models.
3. Parameter Inversion: The authors note novel uses of deep learning in deriving geophysical parameters from SAR data. For example, deep networks have been used to estimate sea ice concentration and to invert surface roughness characteristics.
4. Despeckling: Deep learning approaches to reduce speckle noise leverage residual learning strategies and CNN architectures. These methods have demonstrated the capability to outperform traditional filtering approaches by preserving image details while effectively removing noise.
5. SAR Interferometry (InSAR): Although still in its nascent stages, the integration of deep learning within InSAR applications offers potential advancements in DEM generation and deformation monitoring by leveraging predictive models capable of detecting subtle changes in SAR interferograms.
6. SAR-Optical Data Fusion: The fusion of SAR and optical data aims to capitalize on their complementary information for improved interpretation and analysis. Techniques utilizing deep learning to register and match SAR and optical imagery reflect continuing research in this area.

Future Directions

The study emphasizes the importance of continued exploration in several directions:

• Development of Extensive Benchmark Datasets: Large, standardized datasets appropriate for training deep learning models are critical. This remains a bottleneck in expanding the utility of these methods within the SAR domain.
• Exploration of Unsupervised and Semi-Supervised Learning: Due to the scarcity of labelled SAR datasets, unsupervised learning techniques, including self-supervised and semi-supervised approaches, are encouraged.
• Tailored Deep Learning Architectures: Adapting architectures to become phase-aware while retaining the complex signal integrity inherent in SAR data will be crucial for unlocking the full potential of deep learning in this field.
• Uncertainty Quantification: Integrating probabilistic models to capture both aleatoric and epistemic uncertainties in predictions remains an area for future development, especially in high-stakes applications where decision-making is critical.

In conclusion, this paper provides a comprehensive survey on the integration of deep learning within SAR processing workflows. By outlining current successes and indicating future directions, it lays the groundwork for leveraging deep learning to advance SAR technology further, ultimately contributing to more sophisticated and automated Earth observation solutions.