我实验室教师参加2015 12th International Conference on Fuzzy Systems and Knowledge
Discovery 学术会议,在会上宣读论文Remote sensing image feature selection based on rough set theory and
multi-agent system
摘要如下:
Remote sensing image classification is a very
important method to obtain the geographic information. For a better land cover
classification, it is necessary to bring in more spatial information as
auxiliary. While more spatial information may also lead to the over-fitting of
the classifier algorithm, which, especially under the circumstance of few
samples, will in return devalues classification quality. Select useful features
are very important for remote sensing classification. The traditional rough set
based feature selection algorithms utilize greedy search method which unstable
and relay on initial feature input sequence. This study presents a
classification method based on rough set and multi-agent system. Experiments
show that, compared to the traditional way, the proposed method can be used to
optimize the spatial attributes better for classification and improve the
classification accuracy, with a high application value for the remote sensing
image supervised classification.