ADAPTIVE MUITILEVEL CLASSIFICATION AND DETECTION IN MULTISPECTRAL IMAGES

A novel multilevel adaptive pixel classification and detection (AMLCD) method for detecting pixel and subpixel-size targets for multispectra l images is presented. The AMLCD method takes into account both spectr al and spatial characteristics of the data. In the first level of proc essing, the principal background end members are obtained using the K- means clustering method, Each pixel is examined next for classificatio n using a minimum-distance classifier with the principal end members o btained in the previous level. In the second level, the neighborhood O f each unclassified pixel is analyzed for inclusion of candidate end m embers in an unmixing procedure, If the list of candidate background c lasses is empty, the conditions for their inclusion are relaxed, The f ractions of neighborhood and target signatures for the unclassified pi xels are determined by means of a linear least-squares method in the t hird level. If the results of unmixing are not satisfactory, the list of candidate clusters is renewed. Target detection within each pixel i s performed next, The last processing level determines the size and lo cation of detected targets with a clustering analysis methodology. Tar get size and location are estimated on the basis of the sum and weight ed vector mean, respectively, of the mixing fractions of the neighbori ng pixels, The AMLCD method was successfully applied to both synthetic and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspec tral imagery datasets. (C) 1996 Society of Photo-Optical Instrumentati on Engineers.