Hidden Markov Models for Classifying SAR Target Images

摘要

The classification of three types of ground vehicle targets from the MSTAR (Moving and Stationary Target Acquisition and Recognition) database is investigated using hidden Markov models (HMMs) and synthetic aperture radar images. The HMMs employ training sets of six power spectrum features extracted from High Range Resolution (HRR) radar signal magnitude versus range profiles of the targets for uniform sequences of aspect angles (7 degree separation). Classification accuracy versus numbers of hidden states (from 3 to 30), sequence length (3, 10, 15, and 30), and discretization level of the features (10 and 30 levels) is explored using test and validation data. Best classification (94% correct) is achieved for 3 hidden states, a sequence length of 30, and 10 feature levels.