supervised_image_classification

1.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。
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2.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。
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3.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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