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We propose a spectral-spatial linear discriminant analysis method (LDA) for dimensionality reduction in hyperspectral image. The proposed method uses a local scatter of the small neighborhood as a regularizer to incorporate into the objective function of the LDA. The intrinsic idea is to design an optimal linear transformation that makes these samples among the neighborhood approximate the local mean...
A new method for supervised hyperspectral data classification is proposed. In particular, the notion of Stochastic Minimum Spanning Forests (MSFs) is introduced. For a given hyper-spectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step...
Support Vector Machines (SVM) have gained increasing attention due to their classification accuracy, robustness and indifference towards the input data type. Thus, they are widely used in the remote sensing community — and especially among researchers working on hyperspectral datasets. However, since their first publication a lot of enhancements and adaptations have been proposed, many of which aim...
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