Decision fusion based on extended multi-attribute profiles for hyperspectral image classification
In this paper, a new classification strategy for remotely sensed hyperspectral image data is presented and discussed. The proposed approach adopts a decision fusion strategy which combines a well-established classifier, such as the support vector machine (SVM), with the information provided by extended multi-attribute morphological profiles (EMAPs). EMAPs provide a multilevel characterization of an image created by using a sequence of morphological attribute filters to model different kinds of the structural information contained in such image. In our proposed decision fusion strategy, the SVM classifier is first applied to each of the components (which are associated to different attributes) provided by the EMAP. Then, we apply a decision fusion rule to obtain the final classification result. In our experimental results, conducted with a reference urban hyperspectral data sets widely used in the literature, we show that the proposed strategy provides state of the art results (especially when limited training sets are used.)