A covariance-based anomaly detector for polarimetric remote sensing applications

ABSTRACT The proposed paper recommends a new anomaly detection algo rithm for polarimetric remote sensing applications based on the M-Box covariance test by taking advantage of key features found in a multi-polarimetric data cube. The paper demonstrates: 1) that independent polarization measurements contain information suitable for manmade object discrimination from natural clutter; 2) analysis between th e variability exhibited by manmade objects relative to natural clutter; 3) comparison between the proposed M-Box covariance test with Stokes parameters S 0 and S 1 , DoLP, RX-Stokes, and PCA RX-Stokes; and finally 4) the data used for the comparison spans a full 24-hour measurement. Keywords: polarimetric, LWIR, anomaly, detector, SPICE, covariance test 1. INTRODUCTION Passive polarimetric imagery is a very attractive technology for military applications as a result of its ability in discriminating smooth manmade objects from natural clutter backgrounds relative to conventional broadband infrared. This discrimination is possible because smooth objects tend to emit/reflect polarized light in a way that is very different from that of natural clutter independent of the temperature different between the two classes. As a result, there is a multitude of research work in the area of polarization remote sensing applications ranging from material classification [1 and 2], improvised explosive detection (IED) [3], landmine detection [4], and anomaly detection [5]. Anomaly detection algorithms, independen t of the modality chosen, are sought becau se of their mathematical simplicity, robustness, and their ability in detecting known and previously unknown targets in a variety of situations and meteorological conditions. Anomaly detect ors seek to distinguish unusual objects (e .g., military or civilian objects), or anomalies, that are inherently very different from the background (e.g., trees, grass, etc ) that is being imaged without attempting to match target signatures to pre-stored, often outdated, target models. Such algorithms work by modeling the entire scene to a known statistical model with the assumption that any target(s) of interest in the image only occupy a very small number of pixels. A search window then interrogates all the pixels in the scene and assuming that the target(s) of interest statistics are quite different from that of the background, the algorithm pinpoints the target(s) locations as anomalous. No other community has researched and app lied such detectors in the last decade as the engineers and scientist that work in hyperspectral image processing. Their work has proposed a series of anomaly detection techniques such as the RX algorithm [6] (based on the Mahalanobis distance), a kernel-version of the RX algorithm [7], an adaptation of the support vector machine al gorithm to single-class anomaly detection [8], as well as different background characterization methods that complement HS anomaly detection algorithm such as C-means, Gaussian mixture model, and binomial-based random sampling approach [9, 10, and 11]. The driving force in this paper is to develop an anomaly detection algorithm for LWIR polarimetric imagery capable of distinguishing manmade structures, regardless of what those might be, from a natural clutter background while retaining robustness to geometry of illumination, target state (heated or not), and diurnal, meteorological, and seasonal changes. The overall idea and algorithm proposed in this paper diverg es from the conventional use of Stokes information as the input data to a detector by proposing instead the use of independent polarization measurements (e.g., I