Regularization techniques and parameter estimation for object detection in hyperspectral data

The main challenge for the retrieval of information using hyperspectral sensors is that due to the high dimensionality provided by them there is not comparably enough a priori data to produce well-estimated parameters to solve our detection problem. This lack of enough a priori information for an estimation yields to a rank-deficient problem. As a consequence, this leads to an increment in false alarms and increase in the probability of missing throughout the classification process. An approach based on a regularization technique applied to the data collected from the hyperspectral sensor is used to simultaneously minimize the probabilities of false alarms and missing. This procedure is implemented using algorithms that apply regularization techniques by biasing the covariance matrix, which enable the simultaneous reduction of the probability of false alarm and the decrease of the probability of missing; thus, enhancing the Maximum Likelihood parameter estimation.