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首页> 外文期刊>Remote Sensing >Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil
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Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

机译：巴西东南部作物类型制图的多时态Landsat 8图像的自导分割和分类

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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in S?o Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat?8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15?×?15?km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.
机译：只有精心选择的分割参数才能确保基于对象的图像分析（OBIA）的最佳结果。手动定义合适的参数集可能是一种耗时的方法，不一定会导致最佳结果。手动方法的主观性也很明显。因此，在Stefanski等人提出的监督分割中。 （2013年）集成了细分和分类任务。相对于后续分类，直接优化分割。在这项工作中，我们以这项工作为基础，开发了一种完全自动化的工作流程，用于将图像分割和随机森林（RF）分类相结合的有监督的基于对象的分类。从一组固定的随机选择和人工解释的训练样本开始，自动识别合适的分割参数。位于圣保罗州（巴西）的一个亚热带研究场所被用来评估所提出的方法。输入了两个多时相Landsat？8图像马赛克（从2013年8月至2014年1月），以及来自实地考察和VHR（RapidEye）照片解译的训练样本。使用四个15？×？15？km 2 的测试点，将人工解释的农作物作为独立的验证样本，我们证明了该方法可得出可靠的分类结果。在这些样本上（以像素为单位，n≈1百万），在将甘蔗，大豆，木薯，花生等五类分类时，可以达到80％的总体准确度（OA）。我们发现，与从训练样本中获得的袋装OA相比，从四个测试点获得的整体准确性仅略低。在这五个类别中，甘蔗和大豆的分类最佳，而木薯和花生由于时空特征空间的相似性和类别内部的高变异性而经常被错误分类。有趣的是，在大多数情况下，通过RF分类裕度可以正确识别出错误分类的像素，这是分类图的副产品。

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