In this paper, we propose a true unsupervised method to partition large-size images, where the number of classes, training samples, and other a priori information is not known. Thus, partitioning an image without any knowledge is a great challenge. This novel adaptive and hierarchical classification method is based on affinity propagation, where all criteria and parameters are adaptively calculated from the image to be partitioned. It is reliable to objectively discover classes of an image without user intervention and therefore satisfies all the objectives of an unsupervised method. Hierarchical partitioning adopted allows the user to analyze and interpret the data very finely. The optimal partition maximizing an objective criterion provides the number of classes and the exemplar of each class. The efficiency of the proposed method is demonstrated through experimental results on hyperspectral images. The obtained results show its superiority over the most widely used unsupervised and semi-supervised methods. The developed method can be used in several application domains to partition large-size images or data. It allows the user to consider all or part of the obtained classes and gives the possibility to select the samples in an objective way during a learning process.

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Providing unbiased ground truths for large size images is a complex task that is difficult to achieve in practice. The aim of our method is to easily produce reliable ground truths, from images covering large areas while respecting the physical nature of the observed data. The first step localizes all classes existing in a large size image without any a prior knowledge, as well as the samples that make them up. Next, the user selects classes from these unbiased detected classes, in order to build a true ground truth adapted to this application. No transformation is carried out on of characteristics (spectral features) of the hyperspectral image pixels measured objectively by the sensor. To prove the relevance of the learning sample selection method, we use three supervised classification methods requiring training samples. Two hyperspectral images are selected to illustrate the performance of the obtained training samples. The results of the proposed method are also compared to those available of some state of the art deep learning methods.