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In the last few years, integrating spatial information into active learning framework has been gaining growing interest in the remote sensing community to optimize the collection of training sample set for supervised image classification. We address this problem from two directions. One of the directions focus on improving the classifier's performance to reduce the need for training samples. For this...
In the task of hyperspectral image classification, band selection is often adopted to select a subset of informative bands to reduce the computation and storage cost. We propose a supervised band selection method which allows calculation of a discriminative weight for each band. Specifically, we consider discriminative bands as those that contribute more positive scores to a one-class classifier than...
Sparse representation-based classification (SRC) assigns a test sample to the class with minimal representation error via a sparse linear combination of all the training samples, which has successfully been applied to hyperspectral imagery (HSI). Meanwhile, spatial information, that means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral...
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