TY - GEN
T1 - Ship candidates extraction for optical color imagery
AU - Yu, Xinran
AU - Shi, Zhenwei
PY - 2013
Y1 - 2013
N2 - Ship detection is of great significance lowing to its wide applications. In most existing approaches, some predetection methods are often used to extract ship candidates since applying an accurate algorithm throughout the whole image will be time-consuming and could even cause a lot of false alarms. In addition, most related work focuses on panchromatic imagery but only a little attention has been paid to color imagery. Color images contain more discriminative information of ships than panchromatic images, so it will be easier to extract ships in color images. Further, more information also means more potential to implement image enhancement techniques to solve the problem caused by poor illumination, which is very common in optical images. In this paper, with respect to optical color imagery, we propose a new predetection approach to extract ship candidates preliminarily and rapidly using color information. Firstly, an image enhancement algorithm is employed to improve the quality of input images. Then, we regard the color image as a hyperspectral image and extract ship candidates using a hyperspectral algorithm based on spectral signature model. This hyperspectral algorithm, in essence, utilizes the color information of ships, but the color information is processed in a hyperspectral manner. Unlike the commonly used color segment algorithms which focus on the thresholds in color space, this hyperspectral algorithm concerns more on the patterns of color vectors. Experimental results on real dataset indicate that this image enhancement algorithm is quite suitable for remote sensing images and its performance is better than histogram equalization based techniques. In addition, the hyperspectral algorithm also shows good performance in extracting ship candidates in color images, especially for small ships. As a whole, large areas of background can be removed and most ships can be detected. Although some false alarms still remain, the mount of false alarms is decreased greatly.
AB - Ship detection is of great significance lowing to its wide applications. In most existing approaches, some predetection methods are often used to extract ship candidates since applying an accurate algorithm throughout the whole image will be time-consuming and could even cause a lot of false alarms. In addition, most related work focuses on panchromatic imagery but only a little attention has been paid to color imagery. Color images contain more discriminative information of ships than panchromatic images, so it will be easier to extract ships in color images. Further, more information also means more potential to implement image enhancement techniques to solve the problem caused by poor illumination, which is very common in optical images. In this paper, with respect to optical color imagery, we propose a new predetection approach to extract ship candidates preliminarily and rapidly using color information. Firstly, an image enhancement algorithm is employed to improve the quality of input images. Then, we regard the color image as a hyperspectral image and extract ship candidates using a hyperspectral algorithm based on spectral signature model. This hyperspectral algorithm, in essence, utilizes the color information of ships, but the color information is processed in a hyperspectral manner. Unlike the commonly used color segment algorithms which focus on the thresholds in color space, this hyperspectral algorithm concerns more on the patterns of color vectors. Experimental results on real dataset indicate that this image enhancement algorithm is quite suitable for remote sensing images and its performance is better than histogram equalization based techniques. In addition, the hyperspectral algorithm also shows good performance in extracting ship candidates in color images, especially for small ships. As a whole, large areas of background can be removed and most ships can be detected. Although some false alarms still remain, the mount of false alarms is decreased greatly.
KW - Constrained energy minimization
KW - Optical color image analysis
KW - Optical target detection
KW - Ship candidates extraction
UR - https://www.scopus.com/pages/publications/84885214303
U2 - 10.1117/12.2034926
DO - 10.1117/12.2034926
M3 - 会议稿件
AN - SCOPUS:84885214303
SN - 9780819497772
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Symposium on Photoelectronic Detection and Imaging 2013
T2 - 5th International Symposium on Photoelectronic Detection and Imaging, ISPDI 2013
Y2 - 25 June 2013 through 27 June 2013
ER -