Optimization of Object-Based Image Analysis With Random Forests for Land Cover Mapping
Optimization of Object-Based Image Analysis With Random Forests for Land Cover Mapping
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DOI：
10.1109/jstars.2013.2253089
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发表时间：
2013-04
影响因子：
5.5
通讯作者：
Jan Stefanski;B. Mack;B. Waske
中科院分区：
文献类型：
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作者：
Jan Stefanski;B. Mack;B. Waske
基于对象的图像分析的一个先决条件是生成足够的分段。然而，图像分割算法的参数通常是手动定义的。因此，产生理想的分段级别通常是昂贵的，并且取决于用户。利用随机森林算法和一种新的分割算法，提出了一种多时相数据基于对象分类的半自动优化策略。超像素轮廓(SPC)算法用于生成一组不同级别的分割，使用用户定义范围内的各种参数组合。最后，基于RF提供的类似交叉验证的OOB(OOB)误差选择最佳参数组合。因此，可以根据分类精度来评估参数的质量和相应的分割级别，而无需提供额外的独立测试数据。为了评估提出的概念的潜力，我们使用多时相RapidEye和SPOT 5图像对两个研究区的土地覆盖分类进行了重点研究。一种基于生态识别的广泛使用的多分辨率分割算法(MRS)的分类用于比较。实验结果表明，两种分割算法SPC和MRS在准确率和视觉解释方面具有相似的性能。该策略利用OOB误差来选择理想的分割级别，与基于人工的图像分割结果相比，具有相似的分类精度。总体而言，所提出的策略具有可操作性和易操作性，从而节省了超像素轮廓算法的最佳分割参数的结果。
A prerequisite for object-based image analysis is the generation of adequate segments. However, the parameters for the image segmentation algorithms are often manually defined. Therefore, the generation of an ideal segmentation level is usually costly and user-depended. In this paper a strategy for a semi-automatic optimization of object-based classification of multitemporal data is introduced by using Random Forest (RF) and a novel segmentation algorithm. The Superpixel Contour (SPc) algorithm is used to generate a set of different levels of segmentation, using various combinations of parameters in a user-defined range. Finally, the best parameter combination is selected based on the cross-validation-like out-of-bag (OOB) error that is provided by RF. Therefore, the quality of the parameters and the corresponding segmentation level can be assessed in terms of the classification accuracy, without providing additional independent test data. To evaluate the potential of the proposed concept, we focus on land cover classification of two study areas, using multitemporal RapidEye and SPOT 5 images. A classification that is based on eCognition's widely used multiresolution segmentation algorithm (MRS) is used for comparison. Experimental results underline that the two segmentation algorithms SPc and MRS perform similar in terms of accuracy and visual interpretation. The proposed strategy that uses the OOB error for the selection of the ideal segmentation level provides similar classification accuracies, when compared to the results achieved by manual-based image segmentation. Overall, the proposed strategy is operational and easy to handle and thus economizes the findings of optimal segmentation parameters for the Superpixel Contour algorithm.