22 May 2014 An adaptive filtering based on generalized sidelobe cancellation for target detection of hyperspectral images
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Abstract
In the study, we proposed an adaptive filter with multiple constrains based on the generalized sidelobe canceller (GSC) structure for target detection of hyperspectral images. The proposed filtering approach can alleviate the performance degradation in target detection caused by estimation errors in spectral signature of the desired target or some random noise by unknown interference. First, we design an optimal filter to minimize the interference effect with multiple constrains including unit gain response on desired target and null response on undesired targets. The optimal filter can detect the desired target, suppress the undesired targets and minimize the interference effect. Next, an adaptive filter with GSC structure is proposed to transform the constrained minimization problem into an equivalent unconstrained minimization. The structure of GSC contains two branches: the upper branch is a filter with fixed weights wf designed by multiple constrains to reserve the desired target and interference; the lower branch contains a blocking matrix B and an adaptive filter with weights wa. Matrix B blocks the desired target and preserve the interference. The adaptive filter can be designed to minimize the interference effect without constrains. Simulations validate the effectiveness of the proposed adaptive filter with GSC structure which is robust to the random errors in spectral signature of the desired target.
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Lena Chang, Zay-Shing Tang, Yang-Lang Chang, Bormin Huang, "An adaptive filtering based on generalized sidelobe cancellation for target detection of hyperspectral images", Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240G (22 May 2014); doi: 10.1117/12.2055446; https://doi.org/10.1117/12.2055446
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