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Oil spill detection with multiscale conditional adversarial networks with small-data training

journal contribution
posted on 03.08.2021, 22:26 by Y Li, X Lyu, Alejandro FreryAlejandro Frery, P Ren
We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limita-tion, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model’s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.

History

Preferred citation

Li, Y., Lyu, X., Frery, A. C. & Ren, P. (2021). Oil spill detection with multiscale conditional adversarial networks with small-data training. Remote Sensing, 13(12), 2378-2378. https://doi.org/10.3390/rs13122378

Journal title

Remote Sensing

Volume

13

Issue

12

Publication date

02/06/2021

Pagination

2378-2378

Publisher

MDPI AG

Publication status

Published

Online publication date

18/06/2021

ISSN

2315-4675

eISSN

2072-4292

Article number

ARTN 2378

Language

en