Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 3, Pages 449-453Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2793960
Keywords
Deep forest; region proposal; ship detection; thermal satellite imagery
Categories
Funding
- National Natural Science Foundation of China [61672076, 61331017]
- Army Equipment Research Project [301020203]
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Ship detection from thermal remote sensing imagery is a challenging task because of cluttered scenes and variable appearances of ships. In this letter, we propose a novel detection algorithm named region-based deep forest (RDF) toward overcoming these existing issues. The RDF consists of a simple region proposal network and a deep forest ensemble. The region proposal network trained over gradient features robustly generates a small number of candidates that precisely cover ship targets in various backgrounds. The deep forest ensemble adaptively learns features from remote sensing data and discriminates real ships from region proposals efficiently. The training process of deep forest ensemble is efficient and users can control training cost according to computational resource available. Experimental results on numerous thermal satellite images demonstrate the superior performance of our method compared with state-of-the-art methods.
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