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Offshore Oil Slick Detection: From Photo-Interpreter to Explainable Multi-Modal Deep Learning Models Using SAR Images and Contextual Data., , , , , , and . Remote. Sens., 14 (15): 3565 (2022)SAR Imagery for Detecting Sea Surface Slicks: Performance Assessment of Polarization-Dependent Parameters., , , , , , and . IEEE Trans. Geosci. Remote. Sens., 56 (8): 4237-4257 (2018)Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information., , , , , , and . CoRR, (2022)Offshore Oil Slicks Detection From SAR Images Through The Mask-RCNN Deep Learning Model., , , , , and . IJCNN, page 1-8. IEEE, (2020)Oil Slick Detection in the Offshore Domain: Evaluation of Polarization-Dependent Sar Parameters., , , , , , and . IGARSS, page 8096-8099. IEEE, (2018)Quantum Random Number Generation Using a Quanta Image Sensor., , , , and . Sensors, 16 (7): 1002 (2016)A High Speed Integrated Quantum Random Number Generator with on-Chip Real-Time Randomness Extraction., , , , , and . CoRR, (2021)Explainability of Image Semantic Segmentation Through SHAP Values., , , , , and . ICPR Workshops (3), volume 13645 of Lecture Notes in Computer Science, page 188-202. Springer, (2022)Deep Learning for Semantic Segmentation., , , , and . Multi-faceted Deep Learning, Springer, (2021)Enhancing Change Detection in Spectral Images: Integration of UNet and ResNet Classifiers., , and . ICTAI, page 513-517. IEEE, (2023)