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Very deep learning for ship discrimination in Synthetic Aperture Radar imagery
conference contribution
posted on 2023-05-23, 18:40 authored by Schwegmann, CP, Kleynhans, W, Brian SalmonBrian Salmon, Mdakane, LW, Meyer, RGVEfficient and effective ship discrimination across multiple Synthetic Aperture Radar sensors is becoming more important as access to SAR data becomes more widespread. A flexible means of separating ships from sea is ideal and can be accomplished using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway Networks allow for very deep networks that can be trained using the smaller datasets typical in SAR-based ship detection. A flexible network configuration is possible within Highway Networks due to an adaptive gating mechanism which prevents gradient decay across many layers. This paper presents a very deep High Network configuration as a ship discrimination stage for SAR ship detection. It also presents a three-class SAR dataset that allows for more meaningful analysis of ship discrimination performances. The proposed method was tested on a this SAR dataset and had the highest mean accuracy of all methods tested at 96.67%. The proposed ship discrimination method also provides improved false positive classification compared to the other methods tested.
History
Publication title
Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Editors
IEEEPagination
104-107ISBN
978-1-5090-3333-1Department/School
School of EngineeringPublisher
Curran Associates Inc.Place of publication
Red Hook, New York, United StatesEvent title
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Event Venue
Beijing, ChinaDate of Event (Start Date)
2016-07-10Date of Event (End Date)
2016-07-15Repository Status
- Restricted