Adaptive hyperspectral pixel unmixing using best bands analysis and dc insensitive singular value decomposition
We present a novel approach to two-class linear hyperspectral pixel unmixing. The mixed pixel signatures and training signatures are preprocessed by low pass filtering, and then a best bands analysis determines which bands to use in the final unmixing stage. Abundance estimate statistical properties and partial residues are utilized in adapting the abundance estimate. The well known singular value decomposition method is modified to allow decomposition that is insensitive to DC offsets (DCI_SVD). The proposed method is compared to a best level discrete wavelet transform approach using the Haar mother wavelets [1]. The contributions of this paper are: (1) The unmixing algorithm and (2) The DCI_SVD algorithm. Simulation results using hyperspectral signatures of Cotton, Cogongrass, Johnsongrass, Sicklepod and soil taken with an Analytical Spectral Devices handheld spectroradiometer show the efficacy of the proposed method and its superiority to the DWT approach in the harder unmixing cases.