面向对象图像处理方法在遥感震害提取中的应用研究
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摘要
破坏性地震发生以后,迫切需要对灾害进行准确、快速的评估,为应急指挥、救援决策提供依据。随着遥感技术的不断成熟,其数据获取快速、稳定、不受地面条件制约的特点,使遥感成为震害评估的主要手段之一。遥感震害评估中最关键的步骤是遥感震害的自动识别与提取,它的精度直接影响整个评估的结果。所以,如何提高遥感震害自动识别的精度,是遥感技术在地震应急工作中应用研究的关键。
近年来,高分辨率遥感影像获取技术迅速发展,在基于高分辨率图像的新一轮遥感信息挖掘研究中,传统的基于像元的分类技术已远远不能满足需求,寻找一种能够在光谱信息不高、纹理信息丰富的,复杂的高分辨率遥感影像中充分挖掘有用信息的新技术,成为紧迫的需要。面向对象的遥感图像处理方法,把信息提取的基础由像元换成承纳更多信息的同质像元集合,即影像对象,运用独特的多尺度分割方法,实现图像信息多维的分离。面向对象的图像处理中,除采用光谱信息分析,还加入纹理、形状和相互关系特征的综合分析,并结合模糊逻辑分类方法,交替分类与分割过程,最大程度模拟人类目视解译遥感影像的思维模式,完成目标信息的有效提取。
面向对象图像处理方法在许多领域得到了成功的应用,但在遥感震害提取中的应用尚不成熟。本文借助汶川地震震后遥感图像,深入研究了其在震害提取中的应用。研究首先分析处理了2008年5月12日的汶川8.0级地震灾区的多种遥感数据,由目视解译和遥感影像定性的变化检测入手,从地质灾害到建筑物震害,从宏观变化到细节变化,逐一判断分析,详细研究灾害的特点。并结合野外实地震害考察获得的真实灾害资料,完成全面的精度评价。
在传统的震害分析基础上,论文进一步从原理剖析面向对象图像处理方法,探讨参数、特征的选择,实现面向对象的震害提取。
面向对象的图像处理方法分为两个步骤:影像分割和模糊逻辑分类。
影像分割是建立有层次网络关系结构的影像对象。影像分割应用多尺度分割的方法,在影像整体与像元之间,建立起任意需求尺度下的层次。分割的尺度受到空间分辨率大小的影响;不同的波段权重、同质性系数的选择,也会突出特定的光谱、纹理信息,使分割对象适应不同的需要。
模糊逻辑分类是基于影像分割,将影像对象作为分类单元的分类方法。分类中应用模糊数学理论,将一般事物0与1的划分转变成[0,1]上的划分。模糊逻辑在影像的不同尺度层次间建立起上下级别的语义关系,使对象之间、层次之间实现信息的传递。模糊逻辑分类建立起完整的分类流程,让光谱信息、纹理信息、关系信息平等的互为条件与依据,综合为分类服务。
震害特征复杂多变、遥感数据丰富多样,所以,为了研究面向对象震害提取在不同地面建筑物区域、不同分辨率遥感影像上的应用效果,论文选取北川县城及周边区域、彭州市龙门山镇、都江堰市市区作为研究区域,分别进行快速震害提取、农村建筑物震害提取和城市建筑物震害提取的研究。
在北川县城及周边区域的震害快速提取中,运用分辨率为8米的福卫二号多光谱卫星影像,通过两个分割层次和简单的光谱特征,实现快速的震害范围的提取。
彭州市龙门山镇农村建筑物的震害提取中,针对中国农村建筑物分布分散、结构矮小的特点,运用0.5米航空影像数据,进行单尺度下光谱特征分类,实现建筑物震害提取。
都江堰市市区城市建筑物的震害提取中,充分考虑城市建筑物结构复杂、分布不均匀的特点,从小块影像分类试验着手,应用0.5米航空影像数据进行倒塌建筑物提取。分类过程建立起四个尺度层次,并应用了光谱、形状、相互关系的多种特征。做为对比,研究中还在同一影像上做了基于像元的监督分类。
综合以上研究,本文得到的结论如下:
1.面向对象的遥感震害提取方法可以快速的提取震害,确定震害程度和范围,为灾后应急指挥、救援决策提供依据。-
2.面向对象的建筑物遥感震害提取适用于多种分辨率遥感影像和不同地区(农村或城市),其提取精度不受研究区域变化的影响。
3.与基于像元的震害分类相比,面向对象震害提取可以消除“椒盐效应”等噪声,使分类效果更加理想,分类精度显著提高。
After earthquake occurred, accurate and rapid evaluation of the disaster damage is urgently needed for decision-making of emergency commands and rescues. Remote sensing information is superior to conventional ground survey in such aspects as faster, more stable, less ground condition limits. Recent years, remote sensing earthquake damage evaluation has been one of the major methods, and naturally auto-detection of earthquake damage of remote sensing image has become the critical step in the action. The classification precision directly influenced the whole work result. Therefore, it is important to keep the stability and to improve the precision of the auto earthquake damage detection.
The high spatial resolution remote sensing data has much more complicated texture and less spectral information than other data. With the fast development of high spatial resolution remote sensing techniques, the traditional pixel-based classification method has been far from requirements. It demanded us to find other ways to excavate more useful information from the data. Object-oriented image analysis method solved these problems by changing the basic unit from single pixels to image objects. The special multi-resolution segmentation could extract image object primitives in arbitrary resolution. Additional features such as texture, shape, relationships of the image objects were synthetically using with the spectral information. The method simulated visual interpretation, and adopted fuzzy-logic classification method to accomplish the effective detection.
Object-oriented image analysis method has been successfully applied in various fields. But the application in earthquake damage extraction is still immature. In this paper, further study in earthquake damage extraction of remote sensing data has been realized.
Firstly, this paper analysed a great deal different sourses data of the Ms8.0 Wenchuan earthquake which occurred at May 12th, 2008. The research proceeded a series of qualitative change detection and visual interpretation. The results revealed kinds of damages such as geological hazards and building damage in multi-scales. And for precision evaluation, we also conducted a field study to obtain abundant detail data.
Secondly, on the basis of traditional earthquake damage analysis, object-oriented image analysis method was introduced into the research. A further exploration of the principles was dissected, and study on selection of the parameters and features was carried out.
There are two major parts of the object-oriented image analysis method: image segmentation and fuzzy-logic classification.
Image segmentation constructs a hierarchical network of image objects. The process is able to find image objects in any chosen resolution between the image and pixel levels. Spatial resolution influences the scale choosing, and suitable scale parameters (image layer weights and composition of homogeneity criterion) satisfy particular requires of spectral or texture information.
Fuzzy-logic classification is based on image segmentation, and the image objects are classified as original units. Instead of dividing things by 0 or 1, fuzzy mathematics divides things by the interval [0, 1]. A second semantic network, the class hierarchy, is built up in this part, and the information transmission between classes comes true. The advantage of fuzzy-logic is the possibility to integrate most different kinds of features and to connect them by means of fuzzy- logic operators.
Thirdly, three areas - Beichuan, Longmenshan, and Dujiangyan - were chosen to practise the research for different remote sensing data and various aspects of earthquake damage.
Beichuan county and its surrounding area was used to fast extract earthquake damage area. Through application of two segmentation levels and simple spectral features, the damage area was extracted from the remote sensing data, the spatial resolution of which was 8 m.
Longmenshan town in Pengzhou was chosen aiming at rural building damage extraction. Low buildings are the most common building type in rural China and the buildings always spread dispersedly. Therefore the research chose some simpler classes and fewer scales. The object-oriented classification of Longmenshan was carried out by using of single scale level and spectral features.
Dujiangyan was chosen for city buildings damage extraction. Considering the complicated and uneven characteristics of the city buildings, the research first chose a small cut of the image as sample. The resolution of the remote sensing data is 0.5m, and four segmentation levels were built upon the data to classify. During the process, kinds of features were combined together, and it contained spectral, shape, and relationship features. Traditional supervised classification based on pixels was also operated as control.
Finally, integrated all researches above, some conclusions were gained as follows:
1. Object-oriented classification of remote sensing data is suitable for fast earthquake damage areas extraction. The results could provide evidence for emergency command and rescue decision making.
2. Object-oriented image analysis method could be widely applied in multi-resolution remote sensing data and different areas, such as rural areas and cities, and there is no influence on the classification precision of different areas.
3. Compared to pixel-based classification, the object-oriented classification of earthquake damage could eliminate nosies as“pepper effect”, and it gets stable results towards different areas. The most important is that, to a great extent, object-oriented method improves the precision.
近年来,高分辨率遥感影像获取技术迅速发展,在基于高分辨率图像的新一轮遥感信息挖掘研究中,传统的基于像元的分类技术已远远不能满足需求,寻找一种能够在光谱信息不高、纹理信息丰富的,复杂的高分辨率遥感影像中充分挖掘有用信息的新技术,成为紧迫的需要。面向对象的遥感图像处理方法,把信息提取的基础由像元换成承纳更多信息的同质像元集合,即影像对象,运用独特的多尺度分割方法,实现图像信息多维的分离。面向对象的图像处理中,除采用光谱信息分析,还加入纹理、形状和相互关系特征的综合分析,并结合模糊逻辑分类方法,交替分类与分割过程,最大程度模拟人类目视解译遥感影像的思维模式,完成目标信息的有效提取。
面向对象图像处理方法在许多领域得到了成功的应用,但在遥感震害提取中的应用尚不成熟。本文借助汶川地震震后遥感图像,深入研究了其在震害提取中的应用。研究首先分析处理了2008年5月12日的汶川8.0级地震灾区的多种遥感数据,由目视解译和遥感影像定性的变化检测入手,从地质灾害到建筑物震害,从宏观变化到细节变化,逐一判断分析,详细研究灾害的特点。并结合野外实地震害考察获得的真实灾害资料,完成全面的精度评价。
在传统的震害分析基础上,论文进一步从原理剖析面向对象图像处理方法,探讨参数、特征的选择,实现面向对象的震害提取。
面向对象的图像处理方法分为两个步骤:影像分割和模糊逻辑分类。
影像分割是建立有层次网络关系结构的影像对象。影像分割应用多尺度分割的方法,在影像整体与像元之间,建立起任意需求尺度下的层次。分割的尺度受到空间分辨率大小的影响;不同的波段权重、同质性系数的选择,也会突出特定的光谱、纹理信息,使分割对象适应不同的需要。
模糊逻辑分类是基于影像分割,将影像对象作为分类单元的分类方法。分类中应用模糊数学理论,将一般事物0与1的划分转变成[0,1]上的划分。模糊逻辑在影像的不同尺度层次间建立起上下级别的语义关系,使对象之间、层次之间实现信息的传递。模糊逻辑分类建立起完整的分类流程,让光谱信息、纹理信息、关系信息平等的互为条件与依据,综合为分类服务。
震害特征复杂多变、遥感数据丰富多样,所以,为了研究面向对象震害提取在不同地面建筑物区域、不同分辨率遥感影像上的应用效果,论文选取北川县城及周边区域、彭州市龙门山镇、都江堰市市区作为研究区域,分别进行快速震害提取、农村建筑物震害提取和城市建筑物震害提取的研究。
在北川县城及周边区域的震害快速提取中,运用分辨率为8米的福卫二号多光谱卫星影像,通过两个分割层次和简单的光谱特征,实现快速的震害范围的提取。
彭州市龙门山镇农村建筑物的震害提取中,针对中国农村建筑物分布分散、结构矮小的特点,运用0.5米航空影像数据,进行单尺度下光谱特征分类,实现建筑物震害提取。
都江堰市市区城市建筑物的震害提取中,充分考虑城市建筑物结构复杂、分布不均匀的特点,从小块影像分类试验着手,应用0.5米航空影像数据进行倒塌建筑物提取。分类过程建立起四个尺度层次,并应用了光谱、形状、相互关系的多种特征。做为对比,研究中还在同一影像上做了基于像元的监督分类。
综合以上研究,本文得到的结论如下:
1.面向对象的遥感震害提取方法可以快速的提取震害,确定震害程度和范围,为灾后应急指挥、救援决策提供依据。-
2.面向对象的建筑物遥感震害提取适用于多种分辨率遥感影像和不同地区(农村或城市),其提取精度不受研究区域变化的影响。
3.与基于像元的震害分类相比,面向对象震害提取可以消除“椒盐效应”等噪声,使分类效果更加理想,分类精度显著提高。
After earthquake occurred, accurate and rapid evaluation of the disaster damage is urgently needed for decision-making of emergency commands and rescues. Remote sensing information is superior to conventional ground survey in such aspects as faster, more stable, less ground condition limits. Recent years, remote sensing earthquake damage evaluation has been one of the major methods, and naturally auto-detection of earthquake damage of remote sensing image has become the critical step in the action. The classification precision directly influenced the whole work result. Therefore, it is important to keep the stability and to improve the precision of the auto earthquake damage detection.
The high spatial resolution remote sensing data has much more complicated texture and less spectral information than other data. With the fast development of high spatial resolution remote sensing techniques, the traditional pixel-based classification method has been far from requirements. It demanded us to find other ways to excavate more useful information from the data. Object-oriented image analysis method solved these problems by changing the basic unit from single pixels to image objects. The special multi-resolution segmentation could extract image object primitives in arbitrary resolution. Additional features such as texture, shape, relationships of the image objects were synthetically using with the spectral information. The method simulated visual interpretation, and adopted fuzzy-logic classification method to accomplish the effective detection.
Object-oriented image analysis method has been successfully applied in various fields. But the application in earthquake damage extraction is still immature. In this paper, further study in earthquake damage extraction of remote sensing data has been realized.
Firstly, this paper analysed a great deal different sourses data of the Ms8.0 Wenchuan earthquake which occurred at May 12th, 2008. The research proceeded a series of qualitative change detection and visual interpretation. The results revealed kinds of damages such as geological hazards and building damage in multi-scales. And for precision evaluation, we also conducted a field study to obtain abundant detail data.
Secondly, on the basis of traditional earthquake damage analysis, object-oriented image analysis method was introduced into the research. A further exploration of the principles was dissected, and study on selection of the parameters and features was carried out.
There are two major parts of the object-oriented image analysis method: image segmentation and fuzzy-logic classification.
Image segmentation constructs a hierarchical network of image objects. The process is able to find image objects in any chosen resolution between the image and pixel levels. Spatial resolution influences the scale choosing, and suitable scale parameters (image layer weights and composition of homogeneity criterion) satisfy particular requires of spectral or texture information.
Fuzzy-logic classification is based on image segmentation, and the image objects are classified as original units. Instead of dividing things by 0 or 1, fuzzy mathematics divides things by the interval [0, 1]. A second semantic network, the class hierarchy, is built up in this part, and the information transmission between classes comes true. The advantage of fuzzy-logic is the possibility to integrate most different kinds of features and to connect them by means of fuzzy- logic operators.
Thirdly, three areas - Beichuan, Longmenshan, and Dujiangyan - were chosen to practise the research for different remote sensing data and various aspects of earthquake damage.
Beichuan county and its surrounding area was used to fast extract earthquake damage area. Through application of two segmentation levels and simple spectral features, the damage area was extracted from the remote sensing data, the spatial resolution of which was 8 m.
Longmenshan town in Pengzhou was chosen aiming at rural building damage extraction. Low buildings are the most common building type in rural China and the buildings always spread dispersedly. Therefore the research chose some simpler classes and fewer scales. The object-oriented classification of Longmenshan was carried out by using of single scale level and spectral features.
Dujiangyan was chosen for city buildings damage extraction. Considering the complicated and uneven characteristics of the city buildings, the research first chose a small cut of the image as sample. The resolution of the remote sensing data is 0.5m, and four segmentation levels were built upon the data to classify. During the process, kinds of features were combined together, and it contained spectral, shape, and relationship features. Traditional supervised classification based on pixels was also operated as control.
Finally, integrated all researches above, some conclusions were gained as follows:
1. Object-oriented classification of remote sensing data is suitable for fast earthquake damage areas extraction. The results could provide evidence for emergency command and rescue decision making.
2. Object-oriented image analysis method could be widely applied in multi-resolution remote sensing data and different areas, such as rural areas and cities, and there is no influence on the classification precision of different areas.
3. Compared to pixel-based classification, the object-oriented classification of earthquake damage could eliminate nosies as“pepper effect”, and it gets stable results towards different areas. The most important is that, to a great extent, object-oriented method improves the precision.
引文
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Schwarz M,Steinmeier CH.,Waser L.Detection of storm losses in alpine forest areas by different methodic approaches using high-resolution satellite data[J].21st EARSeL Symposium,Paris,2001.
Sugumaran R,Harken J,Gerjevic J.Using Remote Sensing Data to Study Wetland Dynamics in Iowa[R].Lowa Space Grant (Seed) Final Technical Report,2004.
Willhauck G.,Benz U C,Siegert F.Semiautomatic classification procedures for fire monitoring using multitemporal SAR images and NOAA-AVHRR hotspot data[C].Proceedings of the 4th European Conference on Synthetic Aperture Radar,Cologne,Germany,4-6 June,2002.
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