Image segmentation methods for object-based analysis and classification
Image segmentation methods for object-based analysis and classification
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DOI：
10.1007/978-1-4020-2560-0_12
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发表时间：
2004-01-01
期刊：
影响因子：
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通讯作者：
Pekkarinen, A
中科院分区：
文献类型：
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作者：
Blaschke, T;Burnett, C;Pekkarinen, A
遥感（RS）传感器空间分辨率的不断提高为利用这些信息的应用提出了新的要求。从高分辨率遥感图像中更有效地提取信息并将这些信息无缝地纳入地理信息系统数据库的需要，正在推动地理信息理论和方法进入新的领域。随着地面瞬时视场（GIFOV）的尺寸或像素（像素）尺寸的减小，至少在视觉上可以容易地描绘出更多的精细景观特征。挑战是要产生经过验证的人机方法，具体化和改善人类的解释技能。在这项研究计划中，一些最有前途的成果来自于采用图像分割算法和开发所谓的基于对象的分类方法。在这一章中，我们描述了不同的方法来图像分割和探讨如何分割和基于对象的方法改进传统的基于像素的图像分析/分类方法。根据Schowengerdt（Schowengerdt），传统的图像处理/图像分类方法被称为以图像为中心的方法。在这里，主要目标是产生一个地图，描述感兴趣的现象之间的空间关系。第二种类型是以数据为中心的方法，当用户主要对根据数据值估计个别现象的参数感兴趣时，就采用这种方法。由于图像处理的最新发展，这两种方法似乎正在趋同：从以图像和数据为中心的观点到以信息为中心的方法。例如，对于变化检测和环境监测任务，我们不仅必须从光谱和时间数据维度提取信息。我们还必须将这些估计纳入一个空间框架，并对地理信息系统数据库进行先验和后验利用。决策支持系统必须包含管理者知识、环境/生态知识和规划知识。从技术上讲，这就需要更密切地结合遥感和地理信息系统方法。从本体论上讲，它需要一种新的方法，可以提供一个灵活的，需求驱动的信息生成，因此，分层结构的语义规则描述不同层次的空间实体之间的关系。所涉及的地理信息的几个方面不能通过像素信息本身获得，而只能通过利用感兴趣对象的邻域信息和背景来实现。地物与影像地物的关系
The continuously improving spatial resolution of remote sensing (RS) sensors sets new demand for applications utilising this information. The need for the more efficient extraction of information from high resolution RS imagery and the seamless integration of this information into Geographic Information System (GIS) databases is driving geo-information theory and methodology into new territory. As the dimension of the ground instantaneous field of view (GIFOV), or pixel (picture element) size, decreases many more fine landscape features can be readily delineated, at least visually. The challenge has been to produce proven man-machine methods that externalize and improve on human interpretation skills. Some of the most promising results in this research programme have come from the adoption of image segmentation algorithms and the development of so-called object-based classification methodologies. In this chapter we describe different approaches to image segmentation and explore how segmentation and object-based methods improve on traditional pixel-based image analysis/classification methods.According to Schowengerdt () the traditional image processing/image classification methodology is referred to as an image-centred approach. Here, the primary goal is to produce a map describing the spatial relationships between phenomena of interest. A second type, the data-centred approach, is pursued when the user is primarily interested in estimating parameters for individual phenomena based on the data values. Due to recent developments in image processing the two approaches appear to be converging: from image and data centred views to an information-centred approach. For instance, for change detection and environmental monitoring tasks we must not only extract information from the spectral and temporal data dimensions. We must also integrate these estimates into a spatial framework and make a priori and a posteriori utilization of GIS databases. A decision support system must encapsulate manager knowledge, context/ecological knowledge and planning knowledge. Technically, this necessitates a closer integration of remote sensing and GIS methods. Ontologically, it demands a new methodology that can provide a flexible, demanddriven generation of information and, consequently, hierarchically structured semantic rules describing the relationships between the different levels of spatial entities. Several of the aspects of geo-information involved cannot be obtained by pixel information as such but can only be achieved with an exploitation of neighbourhood information and context of the objects of interest. The relationship between ground objects and image objects