With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing images. Specific fuzzy-based and learning-based algorithms are two broad categories and have the potential to perform well in spectral–temporal domain. In the present study, for mapping paddy fields as a specific class two classification algorithms, viz. fuzzy-based modified possibilistic c-mean (MPCM) algorithm and learning-based 1D-convolutional neural networks (CNN), were tested using Sentinel-2A/2B temporal data. The overall accuracy for learning-based 1D-CNN and fuzzy-based MPCM classifiers was found to be 96% and 93%, respectively. The F-measure values were found to be 0.95 and 0.92 for 1D-CNN- and MPCM-based classifier, respectively. Thus, it can be inferred from this study that the 1D-CNN classifier performed better than the traditional fuzzy-based classifier and can handle heterogeneity within class.

中文翻译：

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一维卷积神经网络与修正的可能性 c 均值算法的比较研究，用于使用时间数据映射移植的稻田

随着高时间分辨率卫星数据可用性的增加，需要一个更强大的分类器，它可以利用遥感图像的时间信息和光谱信息。特定的基于模糊和基于学习的算法是两大类，并且有可能在频谱 - 时间域中表现良好。在本研究中，将稻田映射为特定类别的二级分类算法，即。使用 Sentinel-2A/2B 时间数据测试了基于模糊的改进可能性 c 均值 (MPCM) 算法和基于学习的一维卷积神经网络 (CNN)。发现基于学习的 1D-CNN 和基于模糊的 MPCM 分类器的总体准确率分别为 96% 和 93%。发现基于 1D-CNN 和基于 MPCM 的分类器的 F 测量值分别为 0.95 和 0.92。