遥感图像目标检测
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摘要
遥感图像目标检测在高科技军事对抗中用以获得及时准确的战场信息、捕捉战略打击目标、提供精确的定性定位信息等。在资源探测、环境监测、城市规划等民用领域,也起着举足轻重的作用。本文主要研究了遥感图像的水域提取和桥梁检测,所做的工作包含如下三方面的内容:
(1)提出了一种基于种子点的遥感图像快速河流提取方法。该算法首先根据人工选取的初始种子点确定水域的条件,然后根据初始种子点所处位置不同自动将河流分为两部分,然后由初始种子点开始分别对两部分按顺流速扫方式进行扫描,直至将河流扫描完毕。在扫描过程中如果河流出现分支或者流向发生改变或者存在桥梁类截断,则按加窗搜索法更新种子点和扫描方式继续扫描。该方法特别适合大尺寸的图像(像素点个数超过106数量级)中河流的提取,即使河流目标面积很大时也能满足实时性要求。
(2)提出了一种基于疏散度的高分辨率SAR(Synthetic Aperture Radar, SAR)图像桥梁检测方法。首先提出了一种新的特征——疏散度,根据疏散度将图像分为水域和非水域,并采用Canny算子对得到的二值图的边缘进行修正,然后根据桥梁与水域的位置关系确定感兴趣区域,利用Radon变换和桥梁的几何特性进行直线检测以去除伪桥梁区并对桥梁进行定位。通过对分辨率为1米的SAR图像进行实验,验证了所提算法的有效性。
(3)提出了一种基于小树变换的水域分割和知识驱动的桥梁检测方法。首先对全色高分辨率遥感图像根据桥梁先验知识建立桥梁知识库,利用小树变换进行特征提取并分割水域,随后进行数学形态学运算以连通水域,将连通前后的水域做差得到可能的桥梁片段,然后由可能的桥梁片段检测桥梁候选区,最后进行特征匹配检测出桥梁。通过对分辨率为2.5米的SPOT全色遥感图像进行实验,验证了所提的桥梁检测算法的有效性。
Detecting objects in remote sensing images can be used in obtaining precise information of the war field in time, catching the strike targets, providing precise qualitative and quantitative information and so on. In the field of civilian use, detecting objects also plays an important role, such as resources exploration, environmental monitoring, city planning. We mainly discuss water extraction and bridge detection in remote sensing images, the paper consists of the following three parts:
(1) A river extraction method based on seed point in remote sensing images is proposed. This method firstly determine the water conditions considering the initial seed point selected by manual, then divide the river into two parts and scan the two parts respectively. In the process of scanning, update the seed point and scanning mode if there is embranchment or bridge truncation. The method is especially suitable for large images ( the number of pixels is more than 106 ) , even though the area of river is large.
(2) An approach for detecting bridges over water bodies from high resolution SAR images is presented. Firstly a new feature - porosity is proposed and a method based on porosity combined with an edge mending algorithm utilizing canny edges is used to extract water bodies. By considering the ubiety of bridges and water bodies, the regions of interest are detected. Then the false bridge regions are removed by line detection using Radon transform and the geometric characteristics of bridges. Finally bridges are located in confirmed bridge regions according to water extraction result. The proposed approach has been tested with SAR images that have a spatial resolution of 1 meter. The experimental results demonstrate our method can detect bridges effectively.
(3) An approach based on treelet transform and knowledge for detecting bridges over water bodies is proposed. Firstly we establish bridge knowledge base and segment water by using treelet transform to extract features. After the extraction of water, we connect water bodies using mathematic morphological operation, possible bridge segments and bridge candidate regions are then obtained. Finally bridges are detected by feature matching. The proposed approach has been tested with panchromatic images that have a spatial resolution of 2.5 meters. The experimental results show the method is effective.
(1)提出了一种基于种子点的遥感图像快速河流提取方法。该算法首先根据人工选取的初始种子点确定水域的条件,然后根据初始种子点所处位置不同自动将河流分为两部分,然后由初始种子点开始分别对两部分按顺流速扫方式进行扫描,直至将河流扫描完毕。在扫描过程中如果河流出现分支或者流向发生改变或者存在桥梁类截断,则按加窗搜索法更新种子点和扫描方式继续扫描。该方法特别适合大尺寸的图像(像素点个数超过106数量级)中河流的提取,即使河流目标面积很大时也能满足实时性要求。
(2)提出了一种基于疏散度的高分辨率SAR(Synthetic Aperture Radar, SAR)图像桥梁检测方法。首先提出了一种新的特征——疏散度,根据疏散度将图像分为水域和非水域,并采用Canny算子对得到的二值图的边缘进行修正,然后根据桥梁与水域的位置关系确定感兴趣区域,利用Radon变换和桥梁的几何特性进行直线检测以去除伪桥梁区并对桥梁进行定位。通过对分辨率为1米的SAR图像进行实验,验证了所提算法的有效性。
(3)提出了一种基于小树变换的水域分割和知识驱动的桥梁检测方法。首先对全色高分辨率遥感图像根据桥梁先验知识建立桥梁知识库,利用小树变换进行特征提取并分割水域,随后进行数学形态学运算以连通水域,将连通前后的水域做差得到可能的桥梁片段,然后由可能的桥梁片段检测桥梁候选区,最后进行特征匹配检测出桥梁。通过对分辨率为2.5米的SPOT全色遥感图像进行实验,验证了所提的桥梁检测算法的有效性。
Detecting objects in remote sensing images can be used in obtaining precise information of the war field in time, catching the strike targets, providing precise qualitative and quantitative information and so on. In the field of civilian use, detecting objects also plays an important role, such as resources exploration, environmental monitoring, city planning. We mainly discuss water extraction and bridge detection in remote sensing images, the paper consists of the following three parts:
(1) A river extraction method based on seed point in remote sensing images is proposed. This method firstly determine the water conditions considering the initial seed point selected by manual, then divide the river into two parts and scan the two parts respectively. In the process of scanning, update the seed point and scanning mode if there is embranchment or bridge truncation. The method is especially suitable for large images ( the number of pixels is more than 106 ) , even though the area of river is large.
(2) An approach for detecting bridges over water bodies from high resolution SAR images is presented. Firstly a new feature - porosity is proposed and a method based on porosity combined with an edge mending algorithm utilizing canny edges is used to extract water bodies. By considering the ubiety of bridges and water bodies, the regions of interest are detected. Then the false bridge regions are removed by line detection using Radon transform and the geometric characteristics of bridges. Finally bridges are located in confirmed bridge regions according to water extraction result. The proposed approach has been tested with SAR images that have a spatial resolution of 1 meter. The experimental results demonstrate our method can detect bridges effectively.
(3) An approach based on treelet transform and knowledge for detecting bridges over water bodies is proposed. Firstly we establish bridge knowledge base and segment water by using treelet transform to extract features. After the extraction of water, we connect water bodies using mathematic morphological operation, possible bridge segments and bridge candidate regions are then obtained. Finally bridges are detected by feature matching. The proposed approach has been tested with panchromatic images that have a spatial resolution of 2.5 meters. The experimental results show the method is effective.
引文
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[2]章毓晋.图像分割.北京:科学出版社, 2001: 4-80.
[3] Lillesand T.M. and Kiefer R.W.著,彭望緑,余先川,周涛等译.遥感与图像解译.第四版.北京:电子工业出版社, 2003: 1-34.
[4]戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析.北京:清华大学出版社, 2004: 3-75.
[5]赵英时.遥感应用分析原理与方法.北京:科学出版社, 2003: 309-414.
[6] Maitre H. and Pinciroli M.. Fractal Characterization of a Hydrological Basin Using SAR Satellite Images. IEEE Transactions On Geoscience and Remote Sensing, 1999, 37(1): 175-181.
[7] Niemeyer I., Marpu P.R. and Nussbaum S.. Change detection using the object features. International Geoscience and Remote Sensing Symposium (IGARSS’07), Barcelona, Spain, Jun 23 - 28, 2007: 2374 - 2377.
[8] Inglada J. and Mercier G..A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Muliscale Change Analysis. IEEE Transactions On Geoscience and Remote Sensing. 2007, 45(5): 1432-1444.
[9]左震,张天序,汪国有.远距红外图像中桥梁目标识别方法研究.电子学报, 1998, 26(11): 6-9.
[10] Xu F., Ye X. and Jing R.. Research on knowledge-based bridge recognition method. Pattern Recognition and Artificial Intelligence, 1992, 5(2): 123-128.
[11]陆家驹,李士鸿. TM资料水体识别技术的改进.环境遥感, 1992, 7(1): 17-23.
[12]周成虎,骆剑承.遥感图像地学理解与分析.北京:科学出版社, 2003: 12-185.
[13]李小曼,王刚,田杰. TM影像中水体提取方法研究.西南农业大学学报, 2006, 28(4): 580-582.
[14] Han Y., Zheng H., Cao Q., etal. An Effective Method for Bridge Detection from Satellite Imagery. 2007 Second IEEE Conference on Industrial Electronics and Applications (ICIEA’07). Harbin, China, May 23 - 25, 2007: 2753-2757.
[15]吴樊,王超,张红等.基于知识的中高分辨率光学卫星遥感影像桥梁目标识别研究.电子与信息学报, 2006, 28(4): 587-591.
[16] Baker D.C., Hwang S.S. and Aggarwal J.K.. Detection and Segmentation of Man-Made Objects in Outdoor Scenes: Concret Bridges. Journal of the Optical Society of America: A, 1989, 6(6): 938-950.
[17] Baker D.C., Aggarwal J.K. and Hwang S.S.. Geometry Guided Segmentation of Outdoor Scenes. SPIE Conference on Applications of Artificial Intelligence, Orlando, USA, Apr. 4-6, 1988: 576-584.
[18]徐胜荣,李忠兴.自然景物中桥梁目标识别方法的研究.浙江大学学报, 1995, 29(3): 275-281.
[19]王伟明,徐胜荣,姚庆栋等.一种使用黑板模型实现的图象分割策略及在桥梁目标识别中的应用.计算机学报, 1995, 18(2): 127-130.
[20]姜骊黎,史册,杨海波等.遥感图像中水上桥梁的识别.模式识别与人工智能, 2000, 13(2): 214-217.
[21]侯彪,刘芳,焦李成.高分辨SAR图像中桥梁目标的自动分割.激光与红外, 2004, 34(1): .46-49.
[22]侯彪.脊波和方向信息检测方法及应用.西安电子科技大学博士论文. 2003: 73-105.
[23]秦其明,袁吟欢,陆荣建.卫星图像中不同水体类型识别研究.地理研究, 2001, 20(1): 62-67.
[24] Gonzalez R.C. and Woods R.E.著,阮秋琦,阮宇智译.数字图像处理.第二版.北京:电子工业出版社, 2004: 1-16.
[25]都金康,黄永胜,冯学智等. SPOT卫星影像的水体提取方法及分类研究.遥感学报, 2001, 5(3): 214-219.
[26]邓劲松,王珂,邓艳华等. SPOT-5卫星影像中水体信息自动提取的一种有效方法.上海交通大学学报(农业科学版), 2005, 23(2): 198-201.
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