The paper puts a concept frame of the remote-sensing image mining for the problem, and proposes two content ? based remote-sensing image mining methods : semi-supervised improved fuzzy c-means clustering to remote-sensing image and interactive learning-based image mining in remote sensing 本论文针对这个问题提出了遥感图像挖掘的概念框架,并提出了两种基于内容的遥感图像挖掘方法:采用半监督的改进fcm聚类方法的遥感图像挖掘方法和基于交互学习的遥感图像挖掘方法。
During the program, they will find themselves in a professional and interactive learning environment which allows networking opportunities and the sharing of knowledge and experiences . participants will gain visionary insights which will help them make better decisions in the dynamic local business community 通过积极的参与,学员除可与其他学院分享经验外,更可扩阔其人际脉胳,通过课程中所得之前瞻性洞察力,有助学员于瞬息万变的商业环境中,作出英明的决断。
And it adds a-priori information into the patterns to change the method as a semi-supervised clustering . in the clustering process, the unlabelled patterns compare similarities with the labeled patterns, and then the accuracy of the algorithm can be increased . ( 3 ) the paper proposes an interactive learning-based image mining in remote sensing 由于遥感图像各类别在特征空间中散点图的分布的特点,本文对传统的fcm聚类算法进行改进,并且加入先验信息之后,将原来的非监督的聚类变成一种半监督的聚类方法,通过与已标签的样本进行相似性比较,能有效地提高聚类算法的准确度。