Hyperspectral Image Recognition with Selection of Informative Observations by Conjugacy Criterion

The article proposes a method for hyperspectral image recognition, in whichthe conjugacy with subspaces formed by training class vectors is used as a measure of proximity. The geometric interpretation of the proximity measure used is given in a space formed by eigenvectors. An algorithm for hyperspectral image recognition is built with sequential selection of the most informative subspaces according to the criterion of maximum conjugacy. The results of vegetation recognition experiments on the test hyperspectral image «Indian Pines» that had been obtained within the project AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) are presented. The experiment showed the possibility of achieving higher recognition quality in comparison with known methods.