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Enhanced Ship Detection from Overhead Imagery

机译:增强了从头顶影像检测船只的能力

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In the authors' previous work, a sequence of image-processing algorithms was developed that was suitable for detecting and classifying ships from panchromatic Quickbird electro-optical satellite imagery. Presented in this paper are several new algorithms, which improve the performance and enhance the capabilities of the ship detection software, as well as an overview on how land masking is performed.rnSpecifically, this paper describes the new algorithms for enhanced detection including for the reduction of false detects such as glint and clouds. Improved cloud detection and filtering algorithms are described as well as several texture classification algorithms are used to characterize the background statistics of the ocean texture. These detection algorithms employ both cloud and glint removal techniques, which we describe. Results comparing ship detection with and without these false detect reduction algorithms are provided.rnThese are components of a larger effort to develop a low-cost solution for detecting the presence of ships from readily-available overhead commercial imagery and comparing this information against various open-source ship-registry databases to categorize contacts for follow-on analysis.
机译:在作者先前的工作中,开发了一系列图像处理算法,适用于检测和分类全色Quickbird电光卫星图像中的船只。本文介绍了几种新算法,这些算法可以提高性能并增强船舶检测软件的功能,并概述了如何进行陆地掩蔽.rn具体而言,本文介绍了用于增强检测的新算法,包括用于减少错误检测如闪烁和云。描述了改进的云检测和过滤算法,以及几种纹理分类算法用于表征海洋纹理的背景统计数据。这些检测算法同时采用了我们描述的云和闪烁消除技术。这些结果提供了比较使用和不使用这些错误检测减少算法的船舶检测结果的方法。这些都是开发低成本解决方案的组成部分,该解决方案用于从易于获得的架空商业图像中检测船舶的存在并将此信息与各种开放式服务进行比较。来源船舶注册数据库以对联系人进行分类以进行后续分析。

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