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基于遥感图像的典型目标检测与辅助导航关键技术研究
其他题名Research on Key Technologies of Typical Object Detection and Aided Navigation Based on Remote Sensing Imagery
王彦情
学位类型工学博士
导师谭铁牛 ; 台宪青
2006-12-22
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词遥感图像融合 目标识别 油库检测 城镇道路提取 景象辅助导航 Remote Sensing Image Fusion Object Recognition Oil Tank Detection Urban Road Extraction Image-aided Navigation
摘要随着遥感技术的飞速发展,人类获得的对地观测遥感图像数据越来越多,其中包括不同类型传感器、不同空间分辨率、不同光谱分辨率和不同时间分辨率等多种图像数据,为目标检测与景象导航技术研究提供了丰富的数据资源。基于遥感图像的目标检测和辅助导航已经成为军事领域高度关注的技术,是相关研究的热点问题。然而作为应用性很强的技术,由于应用场景和需求的复杂多变,目前它们还存在很多问题亟待解决。 本文针对基于遥感图像的目标检测和辅助导航中的部分关键技术开展了研究:在目标检测方面,主要围绕油库和道路这两种典型目标开展工作;在景象制导方面,针对目前景象辅助导航算法对匹配区选择要求苛刻的现状,开展了提高环境适应能力的景象辅助导航技术研究。论文的主要贡献如下: 1 针对油库检测,在充分研究油库特征的基础上提出了一种基于合成孔径雷达(Synthetic Aperture Radar, 简称SAR)图像和光学图像融合的检测方法。该方法充分利用油罐在SAR图像中的角反射器效应以及在光学图像中显著的形状特征,并将油罐的物理尺寸有效范围、灰度分布均匀以及群组出现等先验知识有效地融入算法中。结果表明,本方法取得了令人满意的检测性能。 2 针对高分辨率卫星图像中的城镇道路自动提取问题,提出了一种基于机器学习的道路提取框架。结合城镇道路在高分辨率卫星图像中的基本特性,我们从灰度、几何和纹理等多个方面提取特征,并将这些特征作为学习系统的输入,通过学习得到分类器的同时选择出对城镇道路识别比较有效的重要特征。学习得到的这些特征对指导道路的提取有重要参考价值,而且对根据遥感图像分析城镇道路的物理特性有很好的借鉴意义。实验结果表明了该算法的有效性。 3 为了提高景象辅助导航系统的环境适应性,我们提出了一种基于粒子滤波框架的景象辅助导航定位方法。该方法允许飞行器航迹路线上存在一些地貌特征不明显或与周围区域具有相似特征的匹配区,可以降低目前景象辅助导航算法对匹配区过分苛刻的要求。实验表明该方法可获得较为满意的结果。
其他摘要With the rapid development of modern remote sensing technology, a great amount of remote sensing images have been available for earth observation, covering different types of sensors, different spatial resolutions, different spectral resolutions and different temporal resolutions. In recent years, object detection and navigation based on remote sensing images have drawn increasing attention from military and academia, and received a great deal of progress. However, because of the variety of applications, many problems still remain open. Our work mainly focuses on these two respects: object detection and image-aided navigation. As for object detection, we investigate two typical objects: oil tanks and urban roads; and as for navigation, we focus on how to improve the environmental applicability of current image-aided navigation systems. The main contributions of this thesis include: 1 For oil tank detection, we propose an approach by fusing Synthetic Aperture Radar(SAR)and optical images. The presented algorithm takes advantage of the corner cube reflector property of oil tanks, which always makes them bright in SAR images. The distinct geometrical shape of oil tanks in optical satellite images is also an important clue for oil tank detection. Furthermore, prior knowledge, such as the valid range of their physical dimensions, homogenous and group emerging characteristics, is integrated into the automatic detection process. Experimental results demonstrate that the proposed algorithm has an encouraging detection performance. 2 A new framework based on machine learning is proposed for extracting urban roads automatically from high resolution satellite images. Based on urban road properties in high resolution optical images, many features covering intensity, geometrical and textural properties are extracted for learning process, through which not only a classifier is obtained but also effective features for urban road recognition are selected. These selected features are helpful for guiding urban road extraction process and for analyzing the urban road physical properties. Experimental results demonstrate the effectiveness of the proposed method. 3 From the point view of environmental applicability of image-aided navigation, we propose a new approach under the particle filter framework, which can reduce the fastidious demands for matching area selection. Experiments show that it can achieve fairly good results.
馆藏号XWLW1056
其他标识符200318014603028
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5957
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
王彦情. 基于遥感图像的典型目标检测与辅助导航关键技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2006.
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