摘要
土地覆盖是自然环境与人类活动相互作用的中心,而土地覆盖信息主要是通过遥感影像分类来获取,因此影像分类是遥感影像分析的最基本问题之一。在参考基于概率主题模型的高分辨率遥感影像聚类分析的基础上,通过半监督学习最典型的生成模型方法引出了基于概率主题模型的半监督分类(SS-LDA)算法。借鉴SS-LDA模型在文本识别应用的流程,构建了基于SS-LDA算法的高分辨率遥感影像分类的基本流程。通过实验证明,相对于传统的非监督分类与监督分类算法,SS-LDA算法能够获取较高精度的影像分类结果。
Land cover is the center of the interaction of the natural environment and human activities and the acquisition of land cover information are obtained through the classification of remote sensing images, so the image classification is one of the most basic issues of remote sensing image analysis. Based on the image clustering analysis of high-resolution remote sensing image through the probabilistic topic model, the generated model which is a typical method in the semi-supervised learning is analyzed and a classification method based on probabilistic topic model and semi-supervised learning （SS-LDA） is formed in the paper. The process of SS-LDA model used in the text recognition applications is relearned and a basic image classification process of high-resolution remote sensing image is constructed. Comparing to traditional unsupervised classification and supervised classification algorithm, the SS-LDA algorithm will get more accuracy of image classification results through experiments.
出处
《计算机工程与应用》
CSCD
2013年第10期1-4,31,共5页
Computer Engineering and Applications
基金
国家科技支撑计划课题(No.2012BAH27B01)
教育部博士点基金项目(No.20090003120017)
关键词
概率主题模型
高分辨率影像
半监督模型
影像分类
probability topic model
high resolution image
semi-supervised model
image classification
