Unsupervised Remote Sensing Image Classification Using an Artificial DNA Computing

摘要：

In this paper, a spectral encoding and matching algorithm inspired by artificial DNA computing (ADC) is proposed to perform the task of unsupervised classification for hyperspectral remote sensing data. As a novel branch of biological computational intelligence, ADC has strong capabilities of pattern recognition, huge information memory, parallel and fast computation. Unsupervised classification for hyperspectral data is complicated pattern recognition problem with massive volume data. In this paper, unsupervised hyperspectral data classification task by ADC is attempted and the preliminary results are provided. The experiment was performed to evaluate the performance of the proposed algorithm compared with two known algorithms: K-means and ISODATA. It is demonstrated that our method is superior to the traditional algorithms and its overall accuracy and Kappa coefficient reach 80.96％ and 0.7631 respectively. n

关键词： hyperspectral remote sensing DNA computing GEP unsupervised classification.

作者: Hongzan Jiao Yanfei Zhong Liangpei Zhang Pingxiang Li

作者单位: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing Wuhan University, P.R. China

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 1367-1371

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）