Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis



Liu T, Yang Z, Marino A, Gao G & Yang J (2022) Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis. IEEE Transactions on Geoscience and Remote Sensing, 60, Art. No.: 5223519.

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed that performs well in most environments. In this study, a novel subspace form of the PDOF (SPDOF) was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed to the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target clutter ratio (TCR) case.

Polarimetric synthetic aperture radar (PolSAR); Polarimetric detection; Subspace detection; Ship detection; Polarimetric detection optimization filter; Linear Discriminant Analysis; Diagonal loading

IEEE Transactions on Geoscience and Remote Sensing: Volume 60

Publication date31/12/2022
Publication date online28/02/2022
Date accepted by journal04/02/2022

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Dr Armando Marino
Dr Armando Marino

Senior Lecturer in Earth Observation, Biological and Environmental Sciences