Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM
IEEE Transactions on Geoscience and Remote Sensing
Including spatial information is a key step for successful remote sensing image classification. Especially when dealing with high spatial resolution (in both multi-and hyperspectral data), if local variability is strongly reduced by spatial filtering, the classification performance results are boosted. In this paper we consider the triple objective of designing a spatial/spectral classifier which is compact (uses as few features as possible), discriminative (enhance class separation) and robust
... (works well in small sample situations). We achieve this triple objective by discovering the relevant features in the (possibly infinite) space of spatial filters by optimizing a margin maximization criterion. Instead of imposing a filterbank with pre-defined filter types and parameters, we let the model figure out which set of filters is optimal for class separation. To do so, we randomly generate spatial filterbanks and use an active set criterion to rank the candidate features according to their benefits to margin maximization (and thus to generalization) if added to the model. Experiments on multispectral VHR and hyperspectral VHR data show that the proposed algorithm, which is sparse and linear, finds discriminative features and achieve at least the same performances as models using a large filterbank defined in advance by prior knowledge.