Sea Object Detection Using Colour and Texture Classification

Sea target detection from remote sensing
imagery is very important, with a wide array of
applications in areas such as fishery management, vessel
traffic services, and naval warfare. This paper focuses on
the issue of ship detection from spaceborne optical images
(SDSOI). Although advantages of synthetic aperture radar
(SAR) result in that most of current ship detection
approaches are based on SAR images. But disadvantages
of SAR still exist. Such as the limited number of SAR
sensors, the relatively long revisit cycle, and the relatively
lower resolution. To overcome these disadvantages a new
classification algorithm using colour and texture is
introduced for Ship detection. Colour information is
computationally cheap to learn and process. However in
many cases, colour alone does not provide enough
information for classification. Texture information also can
improve classification performance. This algorithm uses
both colour and texture features. In this approach for the
construction of a hybrid colour-texture space we are using
mutual information. Feature extraction is done by the cooccurrence matrix with SVM (Support Vectors Machine)
as a classifier. Therefore this algorithm may attain a very
good classification rate.