Lowpass filter for increasing class separability

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, dimensionality, and classifier type (or discriminant function). To improve classification performance, attention is often focused on seeking improvements on the factors other than class separability because class separability is usually considered inherent and predetermined. The objective of this paper is to call attention to the fact that class separability can be increased. The lowpass filter is proposed as a means for increasing class separability if a data set consists of multipixel objects. In addition, an analysis procedure is proposed in the following order: the lowpass filter, the EM algorithm, feature extraction, and a maximum likelihood classifier. Experiments with hyperspectral data show that increasing class separability compensates for the loss of the classification accuracy caused by the poor statistics estimation due to the small ratio of the training sample size to dimensionality