Unsupervised remote sensing image classification using an artificial DNA computing

Hongzan Jiao, Yanfei Zhong, Liangpei Zhang, Pingxiang Li

Abstract: 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.