Abstract
Ship detection on optical remote sensing images is getting great attention; however, some images called wakes-ship have not been taken into account yet. Current works in ship detection are focusing on in-shore detection where ships are in calm; furthermore, their methods get high Intersection Over Union (IoU), above 70%, but when computing IoU using only wakes-ship images the ratio is 22%. In this paper, it is presented a new framework to improve ship segmentation on wakes-ship images. In order to achieve this goal, it was considered HSV color space and histograms. First, ship detection was done using state-of-the-art ship detection methods. Second, bin histograms in HSV color space was used to get the colors that rely on wakes. Finally, the removal of wakes from ships was done using some discriminative properties. In this way, the increase of the IoU performance at wake-ship segmentation goes from 22% to 63%, which is an improvement of 186%.
Highlights
Ship detection on optical satellite images is attracting great interest with the growing use of optical remote sensing images in recent years [1] and because of its importance in maritime security, fishery management and other applications [1]
There are two kinds of images used in ship detection task, one is off-shore as shown in Fig. 1, where ship detection task appears to be easy and the other is in-shore, Fig. 2, which shows great difficulty when it comes to ship detection because of the land, harbor, and other issues that may happen in optical remote sensing images [1], [6]
The proposed method was tested on 50 wakes-ship images from HRSC2016 dataset, the results are shown in Table III, which shows that ship detection methods on wakes-ship images get better performance when combined with our method
Summary
Ship detection on optical satellite images is attracting great interest with the growing use of optical remote sensing images in recent years [1] and because of its importance in maritime security, fishery management and other applications [1]. There are two kinds of images used in ship detection task, one is off-shore as shown, where ship detection task appears to be easy and the other is in-shore, Fig. 2, which shows great difficulty when it comes to ship detection because of the land, harbor, and other issues that may happen in optical remote sensing images [1], [6]. The tests were run on HRSC20161 dataset released by (Lui’s et al.,2016) [6], it contains 1680 high-resolution optical images of ships in in-shore and off-shore collected from different sources.
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