A Hybrid Automatic Endmember Extraction Algorithm Based on a Local Window

Abstract

Anomaly endmembers play an important role in the application of remote sensing, such as in unmixing classification and target detection. Inspired by the iterative error analysis (IEA), a hybrid endmember extraction algorithm (HEEA) based on a local window is proposed in this paper, which focuses on improving the accuracy of endmember extraction. HEEA uses the spectral-information-divergence-spectral-angle-distance metric to measure the similarity and the orthogonal subspace projection (OSP) method to search for the endmembers, which can decrease the correlation between extracted endmember spectra. Moreover, it is based on a local window which integrates both spatial and spectral aspects to extract endmembers. A synthetic image and Airborne Visible/Infrared Imaging Spectrometer data were tested with the HEEA method, classical IEA, OSP, simplex growing algorithm, sequential maximum angle convex cone, and spectral spatial endmember extraction automatic endmember extraction method. Experimental results indicated that HEEA manifested a slightly better improvement in the rmse and spectrum information than the other methods. The effect was investigated with various SNRs and different window sizes. The robustness of HEEA is better than the classical IEA, even with lower SNR.