A sampling workflow based on unsupervised clusters and multi-temporal sample interpretation (UCMT) for cropland mapping
A sampling workflow based on unsupervised clusters and multi-temporal sample interpretation (UCMT) for cropland mapping
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DOI：
10.1080/2150704x.2018.1500045
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发表时间：
2018-08
影响因子：
2.3
通讯作者：
Pengyu Hao;Huajun Tang;Zhongxin Chen;Le Yu;Mingquan Wu
中科院分区：
文献类型：
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作者：
Pengyu Hao;Huajun Tang;Zhongxin Chen;Le Yu;Mingquan Wu
摘要准确的耕地制图是生态系统服务功能和土地覆盖变化监测等研究的重要输入，样本训练样本的代表性对耕地制图精度有重要影响。本研究旨在提出一种新的基于无监督聚类和多时相解译（UCMT）的耕地制图采样流程。使用迭代自组织数据分析（ISODATA）对每月合成图像时间序列进行无监督聚类，并使用从Ramdom Forest（RF）计算的Gini重要性得分选择最佳时间相位。生成每个聚类的训练样本，并对最佳时间相位进行视觉解释。利用相应的训练样本识别出各时相的耕地，并将多时相耕地结果融合生成耕地图。在两个研究区域的结果表明，使用UCMT生成的训练样本有很好的潜力，以识别农田的总体准确率高于94%，在这两个研究区域。此外，与随机生成的训练样本相比，UCMT样本受训练样本大小的影响较小，在100个训练样本的情况下，Producer的准确率和User的准确率均高于80%。
ABSTRACT Accurate cropland maps are important input for various proposes, such as ecosystem service and land cover change monitoring, and the representativeness of sample training samples influence the cropland mapping accuracy significantly. This study aims to propose a new sampling workflow based on unsupervised cluster and multi-temporal interpretation (UCMT) for cropland mapping. The monthly composited image time series were unsupervised clustered using the Iterative Self organizing Data Analysis (ISODATA) and optimal temporal phases were selected using the Gini importance score calculated from Ramdom Forest (RF). Training samples for each cluster were generated and visually interpreted for the optimal temporal phases. The cropland of each temporal phase was identified using the corresponding training samples, and the cropland maps were generated by merging multi-temporal cropland results. Results in two study regions showed that training samples generated using UCMT had good potential to identify cropland with overall accuracies higher than 94% in both study regions. In addition, comparing with randomly generated training samples, UCMT samples were less affected by training sample size as Producer’s accuracies and User’s accuracies were higher than 80% when 100 training samples used.