The manifold learning theory is firstly used to transform the hyperspectral images into a low-dimension feature spaces.
The reconstruction error is computed to get discriminative information. Then a structured matched subspace detector is
developed. This method can effectively avoid the contamination by targets and spectral anomalies to backgrounds
subspace and detect sub-pixel targets with better performance than traditional methods.
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