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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >FROM PIXELS TO GRIXELS: A UNIFIED FUNCTIONAL MODEL FOR GEOGRAPHIC OBJECT-BASED IMAGE ANALYS
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FROM PIXELS TO GRIXELS: A UNIFIED FUNCTIONAL MODEL FOR GEOGRAPHIC OBJECT-BASED IMAGE ANALYS

机译：从像素到像素：基于地理对象的图像分析的统一功能模型

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Geographic Object-Based Image Analysis (GEOBIA) aims to better exploit earth remotely sensed imagery by focusing on building image-objects resembling the real-world objects instead of using raw pixels as basis for classification. Due to the recentness of the field, concurrent and sometimes competing methods, terminology, and theoretical approaches are evolving. This risk of babelization has been identified as one of the central threats for GEOBIA, as it could hinder scientific discourse and the development of a generally accepted theoretical framework. This paper contributes to the definition of such ontology by proposing a general functional model of the remote sensing image analysis. The model compartmentalizes the remote sensing process into six stages: (i) sensing the earth surface in order to derive pixels which represent incomplete data about real-world objects; (ii) pre-processing the pixels in order to remove atmospheric, geometric, and radiometric distortions; (iii) grouping the pre-processed pixels (prixels) to produce image-objects (grouped pixels or grixels) at one or several scales; (iv) feature analysis to examine and measure relevant spectral, geometric and contextual properties and relationships of grixels in order to produce feature vectors (vexcels) and decision rules for subsequent discrimination; (v) assignation of grixels to pre-defined qualitative or quantitative land cover classes, thus producing pre-objects (preliminary objects); and (vi) post-processing to refine the previous results and output the geographic objects of interest. The grouping stage may be analized from two different perpectives: (i) discrete segmentation which produces well-defined image-objects, and (ii) continuous segmentation which produces image-fields with indeterminate boundaries. The proposed generic model is applied to analyze two specific GEOBIA software implementations. A functional decomposition of discrete segmentation is also discussed and tested. It is concluded that the proposed framework enhances the evaluation and comparison of different GEOBIA approaches and by this is helping to establish a generally accepted ontology.
机译：基于地理对象的图像分析（Geobia）旨在通过专注于与真实世界对象类似的图像对象来更好地利用远程感测的图像，而不是使用原始像素作为分类的基础。由于该领域的近期，并发且有时竞争方法，术语和理论方法正在不断发展。这种禁忌风险已被确定为弥撒的核心威胁之一，因为它可能阻碍科学的话语和普遍接受的理论框架的发展。本文通过提出遥感图像分析的一般功能模型，有助于定义这种本体论。该模型将遥感过程分组为六个阶段：（i）感测地面，以导出代表关于现实世界对象不完整数据的像素; （ii）预处理像素以除去大气，几何和辐射畸变; （iii）将预处理的像素（普拉斯）分组以在一个或多个尺度处生成图像对象（分组像素或纤链）; （iv）特征分析来检查和测量相关频谱，几何和上下文属性和克里克斯的关系，以便产生特征向量（Vexcels）和后续歧视的决策规则; （v）将Grixels分配给预定义的定性或定量陆地覆盖类，从而产生预处理（初步物体）; （vi）后处理以改进先前的结果并输出地理对象的感兴趣。分组阶段可以从两种不同的相比率分析：（i）产生明确定义的图像对象的离散分割，以及（ii）产生具有不确定边界的图像场的连续分割。建议的通用模型应用于分析两个特定的桥桥软件实现。还讨论并测试了离散分割的功能分解。结论是，拟议的框架提高了不同桥鸟类方法的评估和比较，这有助于建立普遍接受的本体。

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