A parallelepiped multispectral image classifier using genetic algorithms
Xiang Mei, Chih‐Cheng Hung, Minh Thi Ngoc Pham, Bor‐Chen Kuo, Todd P. Coleman · 2005
The parallelepiped classifier is one of the widely used supervised classification algorithms for multispectral images. The threshold of each spectral (class) signature is defined in the training data, which is to determine whether a given pixel within the class or not. To avoid involving the analyst for the training data selection, this paper is to study whether the threshold of parallelepiped classifier can be automatically determined by using natural evolution process - genetic algorithms (GAs). In other words, our goal is to create an unsupervised multispectral parallelepiped classifier with the help of genetic algorithms. In this algorithm, we also use a new approach to estimate the initial range. Preliminary experimental results with different parameters for genetic algorithms and a comparison with the supervised parallelepiped classifier are provided.