title: Background first- and second-order modeling for point target detection.
abstract: This paper deals with point target detection in nonstationary backgrounds such as cloud scenes in aerial or satellite imaging. We propose an original spatial detection method based on first- and second-order modeling (i.e., mean and covariance) of local background statistics. We first show that state-of-the-art nonlocal denoising methods can be adapted with minimal effort to yield edge-preserving background mean estimates. These mean estimates lead to very efficient background suppression (BS) detection. However, we propose that BS be followed by a matched filter based on an estimate of the local spatial covariance matrix. The identification of these matrices derives from a robust classification of pixels in classes with homogeneous second-order statistics based on a Gaussian mixture model. The efficiency of the proposed approaches is demonstrated by evaluation on two cloudy sky background databases.
authors.affiliation: Astrium Satellites, Z.I. du Palays, Toulouse, France. laure.genin@onera.fr
authors.forename: Laure
authors.initials: L
authors.lastname: Genin
authors.forename: Frédéric
authors.initials: F
authors.lastname: Champagnat
authors.forename: Guy
authors.initials: G
authors.lastname: Le Besnerais