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The classification of high-dimensional data with too few labeled samples is a major challenge which is difficult to meet unless some special characteristics of the data can be exploited. In remote sensing, the problem is particularly serious because of the difficulty and cost factors involved in assignment of labels to high-dimensional samples. In this paper, we exploit certain special properties...
Sparse representation has significant success in many fields such as signal compression and reconstruction but to the best of our knowledge, no sparse-based classification solution has been proposed in the field of remote sensing. One of the reasons is that the general optimizers are extremely slow, time consuming and needs intensive processing for l1-minimization sparse representation. In this paper,...
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