Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images
- 2 August 2017
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 14 (10), 1665-1669
- https://doi.org/10.1109/lgrs.2017.2727515
Abstract
Ship detection in optical remote sensing imagery has drawn much attention in recent years, especially with regards to the more challenging inshore ship detection. However, recent work on this subject relies heavily on hand-crafted features that require carefully tuned parameters and on complicated procedures. In this letter, we utilize a fully convolutional network (FCN) to tackle the problem of inshore ship detection and design a ship detection framework that possesses a more simplified procedure and a more robust performance. When tackling the ship detection problem with FCN, there are two major difficulties: 1) the long and thin shape of the ships and their arbitrary direction makes the objects extremely anisotropic and hard to be captured by network features and 2) ships can be closely docked side by side, which makes separating them difficult. Therefore, we implement a task partitioning model in the network, where layers at different depths are assigned different tasks. The deep layer in the network provides detection functionality and the shallow layer supplements with accurate localization. This approach mitigates the tradeoff of FCN between localization accuracy and feature representative ability, which is of importance in the detection of closely docked ships. The experiments demonstrate that this framework, with the advantages of FCN and the task partitioning model, provides robust and reliable inshore ship detection in complex contexts.Keywords
Funding Information
- National Natural Science Foundation of China (61671037)
- Beijing Natural Science Foundation (4152031)
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (BUAA-VR-16ZZ-03)
This publication has 18 references indexed in Scilit:
- Ship Detection in Spaceborne Optical Image With SVD NetworksIEEE Transactions on Geoscience and Remote Sensing, 2016
- Salient Band Selection for Hyperspectral Image Classification via Manifold RankingIEEE Transactions on Neural Networks and Learning Systems, 2016
- Vehicle detection in remote sensing imagery based on salient information and local shape featureOptik, 2015
- A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite ImageryIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
- Effective semantic pixel labelling with convolutional networks and Conditional Random FieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Going deeper with convolutionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Fully convolutional networks for semantic segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Multi-class geospatial object detection and geographic image classification based on collection of part detectorsISPRS Journal of Photogrammetry and Remote Sensing, 2014
- Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape FeatureIEEE Transactions on Geoscience and Remote Sensing, 2013
- Saliency Detection by Multiple-Instance LearningIEEE Transactions on Cybernetics, 2013