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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >IMAGE-TO-MAP CONFLICT DETECTION USING ITERATIVE TRIMMING : APPLICATION TO FOREST CHANGE
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IMAGE-TO-MAP CONFLICT DETECTION USING ITERATIVE TRIMMING : APPLICATION TO FOREST CHANGE

机译：使用迭代修剪的图像到地图冲突检测：森林改变的应用

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Large scale vector databases are valued tools for forest management. It is therefore important to keep these databases up to date and various change detection methods have been designed in this aim. Recently, object-based iterative trimming was successfully used to detect change in temperate and tropical forests. The goal of the present study is to transfer this image-to-image method in an image-to-map application. This study focuses on the detection of clear cuts and forest regeneration areas in a multi-spectral Quickbird image. Various steps were necessary to bridge the gap between this image and the vector database. In order to reduce the effects of residual parallax, the vector database was modified along forest boundaries using the viewing parameters of the satellite. Besides, the image was segmented with a large homogeneity constraint in order to produce "pure" image-objects. Eventually, the resulting image-objects were automatically labeled using the information from the modified vector database, and the trimming algorithm was run for each forest class. The hypothesis behind iterative trimming is that objects belonging to the same class share similar characteristics (e.g. spectral reflectance). In other words, they belong to the same distribution. The class distribution was estimated using a non parametric method in order to fit to the data even with complex distributions. The chosen method used kernel density estimates to build the probability density function. Outliers were excluded based on a density threshold and the new parameters of the distribution were reprocessed until the all objects are above the new threshold. The resulting outliers included the majority of the discrepancies between the image and the map in the forest areas. About 50 percent of the forest regeneration and 100percent of the clear cuts were properly detected. It is a promising way to improve semi-automated map updating because the training dataset is the vector database itself. However, further work is needed to test the method on other land cover types and to move from the detection toward the classification of the discrepancies.
机译：大规模矢量数据库是森林管理的价值工具。因此，重要的是要将这些数据库保持迄今为止，并在此目的中设计了各种变更检测方法。最近，基于对象的迭代修剪被成功地用于检测温带和热带林中的变化。本研究的目标是在图像到地图应用程序中将此图像到图像方法转移。本研究重点研究了多光谱Quickbird图像中的清晰切割和森林再生区域的检测。需要各种步骤来弥合该图像与矢量数据库之间的间隙。为了减少残留视差的效果，使用卫星的观察参数沿着森林界限修改了载体数据库。此外，用大的均匀性约束分段，以产生“纯”图像对象。最终，使用从修改的向量数据库中的信息自动标记生成的图像对象，并且为每个林类运行修剪算法。迭代修剪背后的假设是属于同一类的物体共享相似的特征（例如光谱反射率）。换句话说，它们属于相同的分布。使用非参数方法估计类分布，以便拟合到数据，即使具有复杂的分布。所选方法使用内核密度估计来构建概率密度函数。基于密度阈值排除异常值，并重新处理分布的新参数，直到所有对象都高于新阈值。由此产生的异常值包括林区图像与地图之间的大部分差异。检测到大约50％的森林再生和100％的透明切割。这是一种提高半自动地图更新的有希望的方法，因为训练数据集是矢量数据库本身。然而，需要进一步的工作来测试其他土地覆盖类型的方法，并从检测到差异的分类。

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