The image of Nanchuan in 2000 was interpreted using a multi-extracting method combining supervised classification with non-supervised classification.
利用监督分类和非监督分类相结合的分层提取分类方法对南川市2000年遥感影像进行解译。
While for terrace extraction, supervised classification has better result than unsupervised classification. The difference of total accuracy between two methods based on IKONOS image is more than 12%.
而对于IKONOS影像梯田提取，两种分类结果精度相差12%以上，监督分类结果明显优于非监督分类结果。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
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