Learn More
Hyperspectral data potentially contain more information than multispectral data because of higher dimensionality. Information extraction algorithm performance is strongly related to the quantitative precision with which the desired classes are defined, a characteristic which increases rapidly with dimensionality. Due to the limited number of training(More)
A parametric linear feature extraction method is proposed for multiclass classification. The skeleton of the proposed method consists of two types of schemes that are complementary to each other with regard to the discriminant information used. The approximate pairwise accuracy criterion (aPAC) and the common-mean feature extraction (CMFE) are chosen to(More)
In remote sensing, the number of training samples is often limited. For hyperspectral data, it becomes more difficult to obtain accurate estimates of class statistics because of the small ratio of the training sample size to dimensionality. Generally speaking, classification performance depends on four factors: class separability, the training sample size,(More)