UNSUPERVISED AND AUTOMATIC TRAINING SAMPLES SELECTION METHOD

In this paper, we propose a new unsupervised and automatic method for the selection of training samples. Thanks to this completely unsupervised method, the samples to be used in the learning task are selected according to objective criteria. Using biased or simplified training samples does not allow a rigorous explanation of the physical phenomena represented by the acquired data, especially in hyperspectral imaging. Furthermore, the use of training samples in learning task is of great importance and essential because they strongly affect the obtained results of any algorithm, when they are simplified or biased. The proposed method was tested on the public IRIS database and on synthetic and real hyperspectral images. Results show that the proposed method can not only select the training samples but also correct the biased or simplified ground truth.