Automatic Detection of Inshore Ships in High-Resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform
Automatic Detection of Inshore Ships in High-Resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform
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DOI：
10.1109/lgrs.2014.2319082
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发表时间：
2014-05
影响因子：
4.8
通讯作者：
Jian Xu;Xian Sun;Daobing Zhang;Kun Fu
中科院分区：
文献类型：
--
作者：
Jian Xu;Xian Sun;Daobing Zhang;Kun Fu
In this letter, we propose a new detection framework based on robust invariant generalized Hough transform (RIGHT) to solve the problem of detecting inshore ships in high-resolution remote sensing imagery. The invariant generalized Hough transform is an effective shape extraction technique, but it is not adaptive to shape deformation well. In order to improve its adaptability, we use an iterative training method to learn a robust shape model automatically. The model could capture the shape variability of the target contained in the training data set, and every point in the model is equipped with an individual weight according to its importance, which greatly reduces the false-positive rate. Through the iteration process, the model performance is gradually improved by extending the shape model with these necessary weighted points. Experimental result demonstrates the precision, robustness, and effectiveness of our detection framework based on RIGHT.
