首页> 外文会议>International astronautical congress;IAC 2008 >AUTOMATIC ARCHAEOLOGICAL FEATURE EXTRACTION FROM SATELLITE VHR IMAGES BY MATHEMATICAL MORPHOLOGY FUNCTIONS
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AUTOMATIC ARCHAEOLOGICAL FEATURE EXTRACTION FROM SATELLITE VHR IMAGES BY MATHEMATICAL MORPHOLOGY FUNCTIONS

机译:数学形态学功能从卫星VHR图像中自动提取考古特征

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Archaeological applications need a methodological approach on a variable scale able to satisfy the intra-site (excavation) and the inter-site (survey, environmental research). The increased availability of high resolution and micro-scale data has substantially favoured archaeological applications and the consequent use of GIS platforms for reconstruction of archaeological landscapes based on remotely sensed data.Feature extraction of multispectral remotely sensing image is an important task before any further processing. High resolution remote sensing data, specially panchromatic, is an important input for the analysis of various types of image characteristics; it plays an important role in the visual systems for recognition and interpretation of given data. The methods proposed rely on an object-oriented approach based on a theory for the analysis of spatial structures called Mathematical Morphology. The term "morphology" stems on the fact that it aims at analysing object shapes and forms. It is mathematical in the sense that the analysis is based on the set theory, integral geometry, and lattice algebra. Mathematical Morphology has proven to be a powerful image analysis technique; two-dimensional gray tone images are seen as three-dimensional sets by associating each image pixel with an elevation proportional to its intensity level. An object of known shape and size, called the structuring element, is then used to investigate the morphology of the input set. This is achieved by positioning the origin of the structuring element to every possible position of the space and testing, for each position, whether the structuring element either is included or has a nonempty intersection with the studied set. The shape and size of the structuring element must be selected according to the morphology of the searched image structures. These techniques were applied to different archaeological sites in Turkmenistan (Nissa) and in Iraq (Babylon); a further change detection analysis were applied to the Babylon site using two HR images as a pre-post second golf war.We had different results or outputs, taking in consideration that the operative scale of sensed data determines the final result of the elaboration and the output of the information quality, because each of it was sensitive to specific shapes in each input image, we had mapped linear and non-linear objects, updating archaeological cartography, automatic change detection analysis for the Babylon site. The discussion of these techniques has the objective to providethe archaeological team with new instruments for the orientation and the planning of a remote sensing application.
机译:考古学应用需要一种可变规模的方法论方法,该方法论能够满足现场(挖掘)和现场间(调查,环境研究)的需要。高分辨率和微尺度数据的可用性不断提高,极大地促进了考古应用,并因此使用GIS平台基于遥感数据重建考古景观。 在进行任何进一步处理之前,多光谱遥感图像的特征提取是一项重要的任务。高分辨率遥感数据,特别是全色遥感数据,是分析各种类型图像特征的重要输入;它在视觉系统中对给定数据的识别和解释中起着重要的作用。提出的方法依赖于一种基于对象的方法,该方法基于一种称为“数学形态学”的空间结构分析理论。术语“形态”基于这样一个事实,即它旨在分析​​对象的形状和形式。从某种意义上说,分析是基于集合论,积分几何和格代数的数学上的解释。数学形态学已被证明是一种强大的图像分析技术。通过将每个图像像素与与其强度级别成正比的高程相关联,可以将二维灰度图像视为三维集。然后使用形状和大小已知的对象(称为结构元素)来研究输入集的形态。这是通过将结构元素的原点定位到空间的每个可能位置并针对每个位置测试结构元素是否包含或与研究对象集具有非空交集来实现的。必须根据搜索到的图像结构的形态选择结构元素的形状和大小。这些技术被应用于土库曼斯坦(尼萨)和伊拉克(巴比伦)的不同考古现场;使用第二张HR图像作为第二次高尔夫战争之前,对巴比伦场地进行了进一步的变化检测分析。 考虑到感测数据的可操作规模决定了最终的加工结果和信息质量的输出,我们得出了不同的结果或输出,因为它们每个都对每个输入图像中的特定形状敏感,因此我们绘制了线性图。和非线性物体,更新考古学制图,对巴比伦遗址进行自动变化检测分析。对这些技术的讨论旨在提供 考古团队使用新仪器进行遥感应用的定位和规划。

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