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Deep models with multiple layers have demonstrated their potential in learning abstract and invariant features for better representation and classification of remote sensing images. Moreover, metric learning (ML) is usually introduced into the deep models to further increase the discrimination of deep representations. However, the usual deep ML methods treat the training samples in each training batch...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
In this work, a diversified deep structural metric learning is proposed for remote sensing image classification. Firstly, a deep structural metric learning is introduced to take full advantage of structural information of training batches. Secondly, we impose a diversity regularization over the factors of deep structural metric learning to encourage them to be uncorrelated, such that each factor tends...
This paper presents a probabilistic fuzzy method for emitter identification (EID) based on the data-driven model. The input attributes of the EID problem include the radio frequency (RF), pulse repetition interval (PRI), pulse width (PW), etc. Given a fuzzy partition of the input attributes, a method for deriving a set of probabilistic fuzzy rules from training data is presented. With the aid of genetic...
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