...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features
【24h】

A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features

机译：基于形状和纹理特征的星载光学图像分层探测新方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Ship 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, disadvantages of SAR still exist, such as the limited number of SAR sensors, the relatively long revisit cycle, and the relatively lower resolution. With the increasing number of and the resulting improvement in continuous coverage of the optical sensors, SDSOI can partly overcome the shortcomings of SAR-based approaches and should be investigated to help satisfy the requirements of real-time ship monitoring. In SDSOI, several factors such as clouds, ocean waves, and small islands affect the performance of ship detection. This paper proposes a novel hierarchical complete and operational SDSOI approach based on shape and texture features, which is considered a sequential coarse-to-fine elimination process of false alarms. First, simple shape analysis is adopted to eliminate evident false candidates generated by image segmentation with global and local information and to extract ship candidates with missing alarms as low as possible. Second, a novel semisupervised hierarchical classification approach based on various features is presented to distinguish between ships and nonships to remove most false alarms. Besides a complete and operational SDSOI approach, the other contributions of our approach include the following three aspects: 1) it classifies ship candidates by using their class probability distributions rather than the direct extracted features; 2) the relevant classes are automatically built by the samples’ appearances and their feature attribute in a semisupervised mode; and 3) besides commonly used shape and texture features, a new texture operator, i.e-n-n., local multiple patterns, is introduced to enhance the representation ability of the feature set in feature extraction. Experimental results of SDSOI on a large image set captured by optical sensors from multiple satellites show that our approach is effective in distinguishing between ships and nonships, and obtains a satisfactory ship detection performance.
机译：遥感图像中的船舶检测非常重要，在渔业管理，船舶交通服务和海战等领域具有广泛的应用。本文着重于从星载光学图像（SDSOI）进行船舶探测的问题。尽管合成孔径雷达（SAR）的优势导致目前大多数舰船检测方法都基于SAR图像，但SAR的缺点仍然存在，例如SAR传感器数量有限，重访周期相对较长以及相对较低解析度。随着光学传感器数量的不断增加以及由此带来的对光学传感器连续覆盖的改善，SDSOI可以部分克服基于SAR的方法的缺点，应进行研究以帮助满足船舶实时监控的要求。在SDSOI中，云，海浪和小岛等几个因素会影响船舶检测的性能。本文提出了一种新颖的基于形状和纹理特征的层次完整且可操作的SDSOI方法，该方法被认为是错误警报的从粗到精的顺序消除过程。首先，采用简单的形状分析，以消除由具有全球和本地信息的图像分割所产生的明显的虚假候选，并提取具有尽可能低的警报缺失的船舶候选。其次，提出了一种基于各种特征的新型半监督分层分类方法，以区分船舶和非船舶，以消除大多数虚假警报。除了完整且可操作的SDSOI方法之外，我们方法的其他贡献包括以下三个方面：1）通过使用其类别概率分布而不是直接提取的特征对候选船舶进行分类； 2）在半监督模式下，根据样本的外观及其特征属性自动构建相关类别； 3）除了常用的形状和纹理特征，还引入了一种新的纹理算子，即n-n，局部多个图案，以增强特征集在特征提取中的表示能力。 SDSOI在由多颗卫星的光学传感器捕获的大图像集上的实验结果表明，我们的方法可有效区分船舶和非船舶，并获得令人满意的船舶检测性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号