Authors:
Zikun Liu
1
;
Liu Yuan
2
;
Lubin Weng
3
and
Yiping Yang
3
Affiliations:
1
Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences, China
;
2
China Academy of Electronics and Information Technology, China
;
3
Institute of Automation Chinese Academy of Sciences, China
Keyword(s):
High Resolution Optical Remote Sensing Image, Sea-land Segmentation, Ship Detection, Ship Recognition, Dataset.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Classification
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Understanding
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Object Recognition
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Theory and Methods
Abstract:
Ship recognition in high-resolution optical satellite images is an important task. However, it is difficult to recognize ships under complex backgrounds, which is the main bottleneck for ship recognition and needs to be further explored and researched. As far as we know, there is no public remote sensing ship dataset and few open source work. To facilitate future ship recognition related research, in this paper, we present a public high-resolution ship dataset, ``HRSC2016'', that covers not only bounding-box labeling way, but also rotated bounding box way with three-level classes including ship, ship category and ship types. We also provide the ship head position for all the ships with ``V'' shape heads and the segmentation mask for every image in ``Test set''. Besides, we volunteer a ship annotation tool and some development tools. Given these rich annotations we perform a detailed analysis of some state-of-the-art methods, introduce a novel metric, the separation fitness (SF), that
is used for evaluating the performance of the sea-land segmentation task and we also build some new baselines for recognition. The latest dataset can be downloaded from ``http://www.escience.cn/people/liuzikun/DataSet.html''.
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