Adaptive subspace band selection method based on spectrum characteristics

Adaptive subspace band selection method based on spectrum characteristics was proposed to solve the problems, including the existing methods which couldn't divide subspace by studied features and background features easily affected the result of subspace division.Spectral adaptive factor (SAF) was established with the spectral curves of studied features, and the whole data space was divided into some subspace based on clustering. In each subspace, Jeffreys-Matusita distance was calculated to choose the maximum class separability band as the optimal band. The optimal bands combination was achieved.With the data of acousto-optic tunable filter (AOTF) imaging spectrometer,an experiment was accomplished to compare with other band selection methods, involving band index (BI) method and the optimal bands selection method based on the classes distinguish ability. Experimental results show that the optimal bands combination of the proposed method contains better performance and studied features are shown more significant difference. And the average of Jeffreys-Matusitadistance of all classes of the proposed method is the greatest of all methods.Maximum likelihood classification method was also implemented on the images of the optimal bands combination of the proposed method. As a result, the overall accuracy is 96.8% and Kappa coefficient is 0.89.The experiment indicates that the proposed method is effectiveness and practicability.