Drăguţ L, Csillik O, Eisank C, Tiede D
Department of Geography, West University of Timişoara, V. Pârvan Blv. 4, 300223 Timişoara, Romania.
Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria.
ISPRS J Photogramm Remote Sens. 2014 Feb;88(100):119-127. doi: 10.1016/j.isprsjprs.2013.11.018.
We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis.
我们引入了一种新的自动化方法来对多层多尺度图像分割进行参数化，并将其实现为eCognition®软件的通用工具。该方法依赖于局部方差（LV）检测地理空间数据中尺度转换的潜力。该工具检测添加到项目中的层数，并使用多分辨率分割算法以自下而上的方式对它们进行迭代分割，其中分割中的尺度因子，即尺度参数（SP），以恒定增量增加。计算所有层中对象的平均LV值，并将其用作停止迭代的条件：当一个尺度级别记录的LV值等于或低于前一个值时，迭代结束，并保留前一级别分割的对象。SP滞后的三个数量级产生相应数量的尺度级别。对超高分辨率图像的测试为通用适用性提供了令人满意的结果。该工具在实现地理空间图像分析的客观性和自动化方面具有巨大潜力。
