Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks (1802.01445v2)

Abstract: Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic Aperture Radar satellites can provide high resolution topographical maps. However roads are difficult to identify in these data as they look visually similar to targets such as rivers and railways. Most road extraction methods on Synthetic Aperture Radar images still rely on a prior segmentation performed by classical computer vision algorithms. Few works study the potential of deep learning techniques, despite their successful applications to optical imagery. This letter presents an evaluation of Fully-Convolutional Neural Networks for road segmentation in SAR images. We study the relative performance of early and state-of-the-art networks after carefully enhancing their sensitivity towards thin objects by adding spatial tolerance rules. Our models shows promising results, successfully extracting most of the roads in our test dataset. This shows that, although Fully-Convolutional Neural Networks natively lack efficiency for road segmentation, they are capable of good results if properly tuned. As the segmentation quality does not scale well with the increasing depth of the networks, the design of specialized architectures for roads extraction should yield better performances.

• The paper evaluates Fully-Convolutional Neural Networks (FCNNs) for road segmentation in SAR images, showing promising results achievable with architectural adjustments.
• Comparing FCN-8s, Deep Residual U-Net, and DeepLabv3+, the study found DeepLabv3+ with adjustments yielded better quality road segmentation.
• The findings suggest FCNNs are promising for semi-automated SAR annotation, but future work should explore specialized architectures or post-processing for improved road network quality.

Road Segmentation in SAR Satellite Images Using Deep Fully-Convolutional Neural Networks

The paper "Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks" authored by C. Henry, S. M. Azimi, and N. Merkle addresses a specialized application of remote sensing technology. It investigates the performance of Fully-Convolutional Neural Networks (FCNNs) in autonomously extracting road networks from high-resolution Synthetic Aperture Radar (SAR) satellite images. Given the rapid pace at which transportation infrastructure evolves, accurate road mapping is crucial for numerous applications, ranging from urban planning and disaster management to optimizing navigation systems.

SAR Imagery Challenges

SAR systems are invaluable for surveying large areas regardless of weather conditions or time of day, providing high-resolution topographical data. However, road identification in SAR imagery is particularly challenging, as roads often appear visually similar to structures like rivers and railway tracks. Traditional methods rely on initial segmentation using computer vision algorithms, subsequently optimizing road networks via graph construction, often through Markov Random Fields, to ensure coherent road network formation. Despite this, few studies have explored leveraging deep learning advancements for improved segmentation accuracy specific to SAR data.

Fully-Convolutional Neural Networks Approach

The authors present a systematic evaluation of FCNNs for road segmentation, exploring architectures such as FCN-8s, Deep Residual U-Net, and DeepLabv3+. Recognizing the challenge of high class imbalance in SAR images, the paper identifies the need for architectural adjustments. Specifically, the introduction of spatial tolerance parameters enhances sensitivity to thin road structures, and class-weighted Mean Squared Error loss functions address road-pixel imbalance, improving segmentation precision.

Key Findings

FCN-8s served as a baseline, offering surprisingly competitive performance, though DeepLabv3+, notably deeper and equipped with advanced features such as dilated convolutions, displayed smoother and less noisy predictions. These modifications facilitated better generalization and convergence speeds, affirming DeepLabv3+ as a robust model for this application. Moreover, the paper identifies limits due to annotation inconsistencies and absence of object awareness, which occasionally resulted in fragmented road predictions.

Implications and Future Scope

The implications of this research are substantial. FCNNs, once finely adjusted, demonstrate promising results in extracting road networks from SAR images, paving the way for more extensive applications in semi-automated remote sensing annotation. However, the paper suggests that specialized FCNN architectures explicitly designed for thin structure extraction may yield superior segmentation quality. Such advancements could enhance applications dependent on timely and accurate geo-spatial information, notably disaster response and urban planning.

The fusion of advanced deep learning techniques with traditional methods offers a new paradigm in remote sensing, with potential advancements in FCNN architectures poised to further refine segmentation outputs. Future investigations might focus on integrating road network reconstruction post-segmentation to ensure seamless object connectivity, ultimately contributing to more accurate and comprehensive cartography solutions.

Conclusion

This paper substantiates FCNNs as capable road candidate extractors in SAR imagery, successfully overcoming significant barriers such as noise and object indistinctness. As these technologies advance, they could significantly impact remote sensing applications, offering reliable data extraction methods to maintain current geolocalization systems and support multiple sectors reliant on accurate topographical data. The prospect of designing specialized networks for this purpose highlights exciting future possibilities in the field of remote sensing technology.