一种新型支持向量机船舶探测方法研究

Novel ship detection method based on support vector machine

在船舶探测领域,需要根据不同的应用场合,采用不同类型的图像识别与探测技术,而海洋环境的可变特征为获取高解析度图像带来了较大困难,使得对于海上船舶图像的探测与识别难以达到良好的效果。为克服这一问题,本文提出一种新型支持向量机船舶探测方法,该方法利用颜色特征对图像块进行特征提取,采用支持向量机进行分类识别,并在分类后进行图像重建来提升分类精度和图像分辨率。仿真结果表明本文的方法能够应用于船舶探测领域,具有较好的可行性和有效性。

In the field of ship detection,different types of images are used depending on the application.The variable characteristics of the sea environment often complicate a precise detection. These characteristics make the extraction of general properties from individual pixels difficult. To overcome this issue,a block division that divides the image into small blocks of pixels which represent small ship or non- ship regions is proposed.For the classification of blocks,a supervised learning algorithm Support Vector Machine( SVM) is trained using color features extracted from the blocks. Once the classification is performed,ship detection is improved using a reconstruction algorithm,which corrects most wrong classified blocks and extracts the detected ships. The simulation result demonstrates the availability and usefulness of proposed method.