We offer a position as an

Early Stage Researcher (M/F/D)

in the Marie Skłodowska-Curie Innovative Training Network Project MENELAOS^NT in the field

Deep learning for SAR data in presence of adversarial samples (ESR15)

Classification of SAR image data remains a challenge. Major difficulties include the scarcity of available data, and the difficulty of semantically interpreting the SAR backscatter signal. Linked to those problems, there are no large-scale, SAR-derived image databases for Remote Sensing image analysis and knowledge discovery. Furthermore, while optical image classification has seen a breakthrough with the advent of Deep Learning methods that require Big Data, SAR-based systems have so far not experienced the same progress, likely because of not enough data associated training labels is available. The nature of the adversarial samples occurring spontaneously depends on the sensor type. In SAR data, for instance, the effect of strong scattering or the model of image formation and physical processes behind need very specific methods for adversarial samples. The images below exemplify the scattering effects and also basic physical model as the influence of Doppler phenomena for moving targets.

Synthesized and actual SAR images of urban areas behaving strong scattering and a trained imaged displaced from the rails due to Doppler effect.

Given the specific nature of these samples, the solutions to avoid their insertion in the training sets or to alleviate their effects must be tailored accordingly.

The main objective of this project is to give solutions for deep learning with spontaneous adversarial samples in the case of SAR data.

The successful candidate will be employed for a maximum period of three years full-time equivalent and receives a generous financial package plus an additional mobility and family allowance according to the rules for Early Stage Researchers (ESRs) in an EU Marie Sklodowska-Curie Actions Innovative Training Networks (ITN). A career development plan will be prepared for each fellow in accordance with his/her supervisor and will include training, planned secondments and outreach activities in partner institutions of the network. The ESR fellows are supposed to complete their PhD thesis by the end of the 3rd year of their employment. For more information please visit the Marie Sklodowska-Curie Actions Innovative Training Networks website.

YOUR TASKS

• Transformation methods for a relevant SAR data representation, in order to avoid insertion of adversarial samples.
• Design of DNNs for SAR data classification in order to achieve a given invariance to spontaneous adversarial samples.
• Projections of features when learning the semantic axes for 3D visualization such to contextually disambiguate the meaning and to ensure a consistent training.

PROFILE

• A Master of Science in Computer Science Science is required. It could comprise to the full range of mathematical, physical, engineering and technology disciplines related to sensor data acquisition and programming
• Proficient English level
• Image analysis, neural networks, programming languages, basic knowledge on optics could be an advantage

PLANNED SECONDMENTS

• DLR, Munich, Germany, Prof. M. Datcu,10 months, theoretical aspects of DNN for SAR images and related topics on the impact of adversarial samples

ADDITIONAL INFORMATION

References

1. Marmanis, Dimitrios, et al. “Artificial generation of big data for improving image classification: A generative adversarial network approach on SAR data.” arXiv preprint arXiv:1711.02010 (2017).
2. Zhao, Juanping, et al. “Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images.” IEEE Transactions on Geoscience and Remote Sensing (2019).
3. Goodwin, Justin A., et al. “Learning Robust Representations for Automatic Target Recognition.” arXiv preprint arXiv:1811.10714 (2018).