Abstract:Training samples are usually very scarce in land cover classification, which challenges many supervised classifiers. To deal with this problem, this work presents a new learning approach, called local softened affine hull (LSAH). One of the most attractive characters of this classifier is its ability to expand the training set through exploiting "virtual" prototypes. During classification, this method utilizes some local prototypes around the query sample to construct SAH manifolds for each class, which are th… Show more
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