Classification of hyperspectral remote sensing data based on DNA computing
高光谱遥感数据的DNA计算分类

Some initial investigations are conducted to employ DNA computing for hyperspectral remote sensing data classification. As a novel branch of computational intelligence, DNA computing expresses rich information of spectral features with DNA encoding, and acquires the most typical DNA encoding of each class by DNA modulating and controlling mechanism. For each pixel of the hyperspectral image, computing the distance between the pixel and the typical DNA sequence, finding the class property of the minimum distance, set the class property of each pixel as the minimum distance class. An experiment was performed to evaluate the performance of the proposed algorithm in comparison with other traditional image matching classification algorithms: binary cording, spectral angles and spectral derivative feature coding (SDFC). It is demonstrated that the proposed algorithm is superior to the three traditional hyperspectral data classification algorithms based on the experiment results.