Objective

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In high-resolution remote sensing images

inshore ship detection has broad application prospects

such as ocean surveillance

fisheries management

and military reconnaissance. However

unlike the ship detection under pure sea background

inshore ship detection is much more challenging

considering the complex background of the port. The main difficulty of inshore ship detection is that the ship and the dock are adjacent in space and similar in color and texture features

thereby introducing difficulties in distinguishing them. Effective methods for this task are scarce. The existing methods can be mainly into three types. The first is based on template matching but prior geographical information of the port is needed

which is often difficult to obtain. The second type is based on the ship contour method

in which the robustness is low and detecting side-by-side ships is difficult. The third type is based on local features of the ship

which often assume that the ship has a V-shaped bow and is powerless to other ships. Other than the existing methods

a method for inshore ship detection using projection analysis is proposed in this paper. Our method is based on the observation that the shoreline of a dock is typically straight and inshore ships are usually anchored along the ship's rail.

Method

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First

the original image is preprocessed by two sibling approaches:one that segments the sea and land and another that extracts edges. For sea-land segmentation

the K-means clustering algorithm and region-growing algorithm are combined to improve the segmentation quality

which is significant for our method. Meanwhile

the original image is processed to a gradient image by Sobel operator

and then the gradient image is segmented to an edge image with the Otsu algorithm. Second

an improved Hough transform is conducted on the edge image to extract the straight lines

among which are dock shorelines. To remove interference lines

we assume that all extracted lines of dock shorelines should only lie on the border of water. Then

we start to search for ships on both sides of the located dock shorelines. Taking one dock shoreline as an example

we project the sea-land segmentation image perpendicular to the dock shoreline direction and obtain a projection curve. If a ship is anchored along the dock shoreline

the projection curve shape is convex. Otherwise

the curve shape is flat. Furthermore

we can locate the ship with a bounding box by analyzing the curve shape conveniently. To separate side-by-side ships

we conduct another projection in the dock shoreline direction and separate these ships by analyzing the peak and valley of the projection curve. Finally

we remove false alarms using features of ship size

aspect ratio

and duty ratio.

Result

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We have randomly chosen 292 high-resolution remote sensing images of 15 different scenes on Google Earth to test our method. The test images have a total of 962 ships

comprising 139 aircraft carriers

794 destroyers

and 29 civilian ships. The resolution of the images ranges from 1 m to 5.5 m. Thus

the ships on these images are variant in scale

orientation

and even brightness. Our method has correctly detected 822 of the 962 ships

comprising 134 aircraft carriers

666 destroyers

and 22 civilian ships. These results represent 85.4% of the total detection rate

96.4% of the aircraft carrier detection rate

and 83.9% destroyer detection rate. Meanwhile

we have 171 false alarm targets

thereby representing a false alarm rate of 17.2%. Results show that if the resolution is limited from 2 m to 4 m

the total detection rate grows up to 93.5% and the false alarm rate decreases to 5.3%. However

our method is sensitive to the quality of sea-land segmentation

which is essential to extracting the straight line features of the dock shorelines. Thus

the detection rate is compromised on very complex backgrounds

as shown in this paper.

Conclusion

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Our method is simple and effective for inshore ship detection tasks. No prior information on the harbors is needed. The method is suitable for detection of inshore ships in variable resolutions and directions. It is robust to ship shapes and side-by-side ships can be detected as well. Given that our method is sensitive to the quality of sea-land segmentation

more powerful segmentation algorithms

which is our future research direction

may be effective.