Testing an automated unsupervised classification algorithm with diverse land covers

We test a new automatic unsupervised classification algorithm designed for hyperspectral images. The algorithm automatically determines the number of clusters in the image by finding dense regions of the pixel cloud. A variation on migrating means clustering is used to find the dense regions. Five scenes from an airborne AVIRIS data set are used to test the algorithm. The algorithm successfully finds the dominant land covers and many areally small land covers, such as roads and other man-made structures.