Classification of the real remotely sensed image covered with clouds

Abstract

In this paper supervised classification method is proposed. It is based on Bayes discriminant functions (BDF) and it deals with the problem of optimal classification for images, which are corrupted by natural phenomenon such as cloud, smoke or fog. Solving such a problem is very important when we have remotely sensed information, which very often is corrupted by clouds. For example, the remotely sensed images from the territory of Lithuania are very often corrupted by clouds. The idea of classification, using BDF with incorporated spatial dependency between the observation to be classified and the training sample is presented in earlier works of the author. The novelty of this paper is the method how to use these methods for the real situation, i.e. for the remotely sensed image which is naturally covered by clouds. Visual and numerical results are presented in this paper, which show the advantage of this method against BDF ignoring spatial dependency between training sample and observation to be classified and against the method using grey level cooccurrence matrices.

Keywords

• image classification
• Gaussian random fields
• Bayes discriminant function
• supervised classification
• semivariance