Zhou Zhen, Huang Jingfeng, Wang Jing, Zhang Kangyu, Kuang Zhaomin, Zhong Shiquan, Song Xiaodong
Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.
Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou, China.
PLoS One. 2015 Nov 3;10(11):e0142069. doi: 10.1371/journal.pone.0142069. eCollection 2015.
Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.
大多数甘蔗种植区位于中国南方。然而，由于该地区生长季节多云的气候以及与其他作物严重的光谱混合，可用的遥感数据有限，因此甘蔗的遥感研究一直受到限制。在本研究中，我们开发了一种利用时间序列中分辨率遥感数据自动绘制大面积甘蔗分布图的方法。为此，使用了两种主要技术，即面向对象方法（OOM）和数据挖掘（DM）。此外，在甘蔗生长期间获取了时间序列的中国环境减灾卫星一号（HJ-1）电荷耦合器件（CCD）图像。使用多分辨率分割算法生成图像对象，并使用AdaBoost算法实施数据挖掘，该算法生成预测模型。将预测模型应用于HJ-1 CCD时间序列图像对象，然后生成甘蔗种植面积图。使用独立的实地调查采样点评估分类精度。混淆矩阵分析表明，总体分类精度达到93.6%，卡帕系数为0.85。因此，结果表明该方法可行、高效，适用于在因经济考虑或合格图像有限而无法应用高分辨率遥感数据的大面积区域推断其他作物的分类。
