基于像斑统计分析的高分辨率遥感影像土地利用/覆盖变化检测方法研究
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摘要
土地资源是人类在地球上赖以生存最重要的资源，它不仅能够反映出一个国家或地区的地表环境等基本地理信息，还能够在一定程度上反映地区经济发展、城镇化和军事布局等状况。近些年来，随着3S技术的不断发展，地理信息服务已经以各种形式深入到人们的日常生活中去，从而对地理信息的获取和快速更新提出了更高的要求。土地利用/覆盖情况是人类和土地相互作用的综合结果，同时作为各种资源管理和地理信息服务所需要的最基本数据，对土地利用/覆盖信息的获取、分析和更新，显得尤为重要。
遥感影像数据以其宏观性、实时性，一直以来都是进行土地利用/覆盖及其变化检测的最重要手段。而本世纪以来，高分辨率遥感影像数据的获取成为可能，使得土地利用/覆盖的变化检测也有了进一步的发展。从研究现状来看，目前基于遥感影像的土地利用/覆盖变化检测方法多种多样，而各种方法的效果根据不同的变化检测需求和实验数据特点而各不相同，尚没有较为成熟的基于影像的变化检测框架和体系出现。目前主要的研究重点仍在变化检测方法的研究和变化检测流程的自动化两个方面。高分辨率遥感影像能够携带更多用于影像分析和变化检测的信息。然而一方面，影像数据量的增加和人工解译效率低成本高的矛盾更加突出；另一方面，由于高分辨率遥感影像本身具有的特点，使得传统的遥感影像分析和变化检测方法并不一定适用于高分辨率遥感数据的分析。可以说高分辨率遥感影像为土地利用/覆盖变化检测提供了更丰富信息的同时也带来了更多挑战。因此，本文应用高分辨率遥感影像，就变化检测方法研究、变化检测流程完整性和自动化方面，展开进一步的探讨和分析。
多源数据的应用，有利于获取更多用于变化检测的信息，这些信息可以以先验知识的形式出现，直接参与到影像分析过程中；同时，变化检测的最终目的往往是用于对现有土地利用图的更新，因此本文首先提出了利用现有土地利用图和遥感影像进行配准套合的影像分析方法。针对高分辨率遥感影像的特性以及人工识别地物的机理，应用像斑代替像素作为影像分析的基本单位，通过矢量图和影像的配准套合，根据土地利用图图斑边界直接获取像斑，从而能够使像斑获取土地利用图中的部分属性信息，同时提取多种有利于影像解译的特征，以此为基础进行后续的影像分析。
由于土地利用和土地覆盖存在类别的不一致性，使得直接通过配准套合获取的影像像斑，并不能保证其内部光谱的同质性，这为后续的基于像斑的影像分析带了的困难。本文探讨和研究了上述的类别不一致性，并通过应用土地利用图辅助的像斑多尺度分割、以及数据挖掘的方法，提出了进一步获取同质性较强的像斑的方法。
接下来，本文将现有的基于遥感影像变化检测的方法分为了分类前比较和分类后处理两种流程，并对各种方法进行了归纳、讨论和实验。在分析各种变化检测方法的优劣势后，提出了一种考虑类别光谱变化规律的变化检测方法，并用实验证明了其可行性。同时提出将ROC曲线应用于分类前比较变化检测方法的阈值获取中去，用于提高变化检测方法的自动化程度。
在此基础上，文章通过构建适用于像斑分析的马尔柯夫随机场图模型以及统计分析的方法，综合应用像斑的响应光谱特征、像斑与其邻域像斑具有的类别空间关系，以及像斑与其历史时期像斑具有的类别时序转移关系，提出了一种综合光谱、空间、时序信息进行变化像斑变化后类别判定的方法，并通过实验验证了其有效性，证实了空间关系和时序关系在影像分析中的重要作用。
最后，本文介绍了基于像斑统计分析的高分辨率遥感影像土地利用变化检测方法系统框架，并对该系统中以实现的主要功能进行了简要介绍。
Land resources are the most important resource for human to live on the earth. It cannot only reflect a country or a region’s surface environment and geography basicsituation, but also can to a certain extent reflect regional economic development,urbanization and military layout and so on. In recent years, along with thedevelopment of3S technology, geographic information service has been engaged intothe people’s daily life in various styles, and the geographical information acquisition,quickly update have been put forward higher demand. Land use/cover situation is thecomprehensive result of the interaction between human and environment, and as thebasic data needed for resource management and geographic information services, theacquisition, analysis and updating of land use/cover information seems particularlyimportant.
The macro and real-time features of the remote sensing image data has always made itto be the most important data source for land use/cover and its change detection, andin this century, land use/cover change detection has a further development due to theavailable of the high resolution remote sensing image data. From the research currentsituation, there are several approaches for land use/cover change detection, and theefficiency of each method depends on the change detection requirements andexperimental data characteristics. Up till now there is not a mature framework orsystem for change detection. At present the research focuses are two aspects: themethod of change detection, and the automation of the method. More informationused for image analysis could be gotten from high resolution remote sensing image,compare to the low resolution images. However, on the one hand, the more prominentcontradiction occurs between the increased amount of image data and the lowefficiency, high cost artificial interpretation; on the other hand, the approaches usedfor analyze low resolution image data may not be appropriate for high resolutionimage analysis. So we can say high resolution remote sensing images bring both advantages and challenges for land use/cover change detection. Therefore, this papergo further researches on both methods of change detection and integrity, automationof change detection process using high resolution remote sensing images.
More information could be extracted for land use/cover detection using multiplesource data, and these data can be regarded as the prior knowledge which can engageinto the process of image analysis directly. Meanwhile, the final purpose of changedetection using remote sensing data is always for updating existing land use map.Thus, this paper used both remote sensing images and existing land use map forchange detection. Image segments are used as the basic units for image analysisaccounting for the characteristics of high resolution images and the mechanism ofartificial objects identification. Through the matching between remote sensing imagesand the land use map, image segments could be gotten directly by the land use patches.Information contained in the attributes table of land use map can be used for thefollow-up image interpretation.
Because of the inconsistence of the existing categories between land use and landcover, the image object obtained directly through matching process could not keep allthe pixels within it homogeneous, which would bring some difficulty to follow-upimage processing. This paper discussed this inconsistence mentioned above, usingmulti-scale image segmentation and data mining methods in order to improve thehomogeneity of the image segment.
Next, this paper divided the change detection process into two methods: beforeclassification comparison and post classification and did a discussion, summarizationand experiments for each method. After analyzing both advantages and disadvantagesof these two methods, this paper gives an approach for land use/cover changedetection accounting for class spectral change rules and proves its validity by anexperiment. Meanwhile ROC curve was used for change detection threshold decisionso as to enhance the automation degree of change detection methods.
On this basis, through the construction of Markov Random Field graph model andusing statistical method, spectral features of the image segments, spatial relationshipamong the image segments and their neighborhood segments, and timing relationship among segments on different period images are used comprehensively for decidingthe new class properties of changed image segments. The experiments proved theefficiency of this method and meanwhile confirmed the important role of spatial andtiming relationship using for remote sensing image interpretation.
Finally, this paper introduced a system framework of high resolution remote sensingimage land use/cover change detection based on the statistical analysis of imagesegments, and also a brief introduction of the main function of this system which havealready achieved.
遥感影像数据以其宏观性、实时性，一直以来都是进行土地利用/覆盖及其变化检测的最重要手段。而本世纪以来，高分辨率遥感影像数据的获取成为可能，使得土地利用/覆盖的变化检测也有了进一步的发展。从研究现状来看，目前基于遥感影像的土地利用/覆盖变化检测方法多种多样，而各种方法的效果根据不同的变化检测需求和实验数据特点而各不相同，尚没有较为成熟的基于影像的变化检测框架和体系出现。目前主要的研究重点仍在变化检测方法的研究和变化检测流程的自动化两个方面。高分辨率遥感影像能够携带更多用于影像分析和变化检测的信息。然而一方面，影像数据量的增加和人工解译效率低成本高的矛盾更加突出；另一方面，由于高分辨率遥感影像本身具有的特点，使得传统的遥感影像分析和变化检测方法并不一定适用于高分辨率遥感数据的分析。可以说高分辨率遥感影像为土地利用/覆盖变化检测提供了更丰富信息的同时也带来了更多挑战。因此，本文应用高分辨率遥感影像，就变化检测方法研究、变化检测流程完整性和自动化方面，展开进一步的探讨和分析。
多源数据的应用，有利于获取更多用于变化检测的信息，这些信息可以以先验知识的形式出现，直接参与到影像分析过程中；同时，变化检测的最终目的往往是用于对现有土地利用图的更新，因此本文首先提出了利用现有土地利用图和遥感影像进行配准套合的影像分析方法。针对高分辨率遥感影像的特性以及人工识别地物的机理，应用像斑代替像素作为影像分析的基本单位，通过矢量图和影像的配准套合，根据土地利用图图斑边界直接获取像斑，从而能够使像斑获取土地利用图中的部分属性信息，同时提取多种有利于影像解译的特征，以此为基础进行后续的影像分析。
由于土地利用和土地覆盖存在类别的不一致性，使得直接通过配准套合获取的影像像斑，并不能保证其内部光谱的同质性，这为后续的基于像斑的影像分析带了的困难。本文探讨和研究了上述的类别不一致性，并通过应用土地利用图辅助的像斑多尺度分割、以及数据挖掘的方法，提出了进一步获取同质性较强的像斑的方法。
接下来，本文将现有的基于遥感影像变化检测的方法分为了分类前比较和分类后处理两种流程，并对各种方法进行了归纳、讨论和实验。在分析各种变化检测方法的优劣势后，提出了一种考虑类别光谱变化规律的变化检测方法，并用实验证明了其可行性。同时提出将ROC曲线应用于分类前比较变化检测方法的阈值获取中去，用于提高变化检测方法的自动化程度。
在此基础上，文章通过构建适用于像斑分析的马尔柯夫随机场图模型以及统计分析的方法，综合应用像斑的响应光谱特征、像斑与其邻域像斑具有的类别空间关系，以及像斑与其历史时期像斑具有的类别时序转移关系，提出了一种综合光谱、空间、时序信息进行变化像斑变化后类别判定的方法，并通过实验验证了其有效性，证实了空间关系和时序关系在影像分析中的重要作用。
最后，本文介绍了基于像斑统计分析的高分辨率遥感影像土地利用变化检测方法系统框架，并对该系统中以实现的主要功能进行了简要介绍。
Land resources are the most important resource for human to live on the earth. It cannot only reflect a country or a region’s surface environment and geography basicsituation, but also can to a certain extent reflect regional economic development,urbanization and military layout and so on. In recent years, along with thedevelopment of3S technology, geographic information service has been engaged intothe people’s daily life in various styles, and the geographical information acquisition,quickly update have been put forward higher demand. Land use/cover situation is thecomprehensive result of the interaction between human and environment, and as thebasic data needed for resource management and geographic information services, theacquisition, analysis and updating of land use/cover information seems particularlyimportant.
The macro and real-time features of the remote sensing image data has always made itto be the most important data source for land use/cover and its change detection, andin this century, land use/cover change detection has a further development due to theavailable of the high resolution remote sensing image data. From the research currentsituation, there are several approaches for land use/cover change detection, and theefficiency of each method depends on the change detection requirements andexperimental data characteristics. Up till now there is not a mature framework orsystem for change detection. At present the research focuses are two aspects: themethod of change detection, and the automation of the method. More informationused for image analysis could be gotten from high resolution remote sensing image,compare to the low resolution images. However, on the one hand, the more prominentcontradiction occurs between the increased amount of image data and the lowefficiency, high cost artificial interpretation; on the other hand, the approaches usedfor analyze low resolution image data may not be appropriate for high resolutionimage analysis. So we can say high resolution remote sensing images bring both advantages and challenges for land use/cover change detection. Therefore, this papergo further researches on both methods of change detection and integrity, automationof change detection process using high resolution remote sensing images.
More information could be extracted for land use/cover detection using multiplesource data, and these data can be regarded as the prior knowledge which can engageinto the process of image analysis directly. Meanwhile, the final purpose of changedetection using remote sensing data is always for updating existing land use map.Thus, this paper used both remote sensing images and existing land use map forchange detection. Image segments are used as the basic units for image analysisaccounting for the characteristics of high resolution images and the mechanism ofartificial objects identification. Through the matching between remote sensing imagesand the land use map, image segments could be gotten directly by the land use patches.Information contained in the attributes table of land use map can be used for thefollow-up image interpretation.
Because of the inconsistence of the existing categories between land use and landcover, the image object obtained directly through matching process could not keep allthe pixels within it homogeneous, which would bring some difficulty to follow-upimage processing. This paper discussed this inconsistence mentioned above, usingmulti-scale image segmentation and data mining methods in order to improve thehomogeneity of the image segment.
Next, this paper divided the change detection process into two methods: beforeclassification comparison and post classification and did a discussion, summarizationand experiments for each method. After analyzing both advantages and disadvantagesof these two methods, this paper gives an approach for land use/cover changedetection accounting for class spectral change rules and proves its validity by anexperiment. Meanwhile ROC curve was used for change detection threshold decisionso as to enhance the automation degree of change detection methods.
On this basis, through the construction of Markov Random Field graph model andusing statistical method, spectral features of the image segments, spatial relationshipamong the image segments and their neighborhood segments, and timing relationship among segments on different period images are used comprehensively for decidingthe new class properties of changed image segments. The experiments proved theefficiency of this method and meanwhile confirmed the important role of spatial andtiming relationship using for remote sensing image interpretation.
Finally, this paper introduced a system framework of high resolution remote sensingimage land use/cover change detection based on the statistical analysis of imagesegments, and also a brief introduction of the main function of this system which havealready achieved.
引文
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