Singha Mrinal, Wu Bingfang, Zhang Miao
University of Chinese Academy of Sciences, Beijing 100049, China.
Division of Digital Agriculture, Institute of Remote Sensing and Digital Earth, Olympic Village Science Park, Beijing 100101, China.
Sensors (Basel). 2016 Dec 22;17(1):10. doi: 10.3390/s17010010.
Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.
准确及时地绘制水稻分布图对于粮食安全和环境可持续性至关重要。本研究评估了从粗分辨率数据中提取的时间特征对基于对象的细分辨率水稻数据分类的效用。首先将粗分辨率植被指数数据与细分辨率数据融合，以生成时间序列细分辨率数据。从融合数据中提取时间特征，并将其与多光谱数据相加，以提高分类精度。时间特征提供作物生长信息，而多光谱数据提供水稻的模式变化。实现的总体分类精度和kappa系数分别为84.37%和0.68。结果表明，使用时间特征使单日期多光谱图像的总体分类精度从65.62%提高到84.37%，提高了18.75%。水稻分类的最小敏感度（MS）也得到了提高。比较表明，绘制的水稻面积与区级农业统计数据相似。这项工作还强调了特征选择对于实现更高分类精度的重要性。这些结果证明了联合使用时间和光谱特征进行准确水稻分类的潜力。
