MCM-AWARE TWIN-LEAST-SQUARE GAN FOR HY-PERSPECTRAL ANOMALY DETECTION Anonymous

Abstract

Hyperspectral anomaly detection under high-dimensional data and interference of deteriorated bands without any prior information has been challenging and attracted close attention in the exploration of the unknown in real scenarios. However, some emerging methods based on generative adversarial network (GAN) suffer from the problems of gradient vanishing and training instability with struggling to strike a balance between performance and training sample limitations. In this work, aiming to remedy the drawbacks of existing methods, we present a novel multi-scale covariance map (MCM)-aware twin-least-square GAN (MTGAN). Instead of the widely used single-scale Gaussian hypothesis background estimation, in MTGAN, we introduce the MCM-aware strategy to construct multi-scale priors with precise second-order statistics, thereby implicitly bridging the spatial and spectral information. Thus, we reliably and adaptively represent the prior of HSI to change the priors-lack situation. Moreover, we impose the twin-least-square loss on GAN, which helps improve the generative ability and training stability in feature and image domains, overcoming the gradient vanishing problem. Finally, the network enforced with a new anomaly rejection loss establishes a pure and discriminative background estimation. Experiments demonstrate that the average detection accuracy of MTGAN reaches 0.99809, which is superior to the state-ofthe-art algorithms.

1. INTRODUCTION

Hyperspectral image (HSI) appears as a three-dimensional (3D) data cube, two dimensions of which show the spatial information of materials, and the other reveals hundreds of contiguous bands to perceive each scene (Yokoya et al., 2012) . Among a wealth of HSIs interpretation techniques in practical situations, anomaly detection has many potential applications in video surveillance, activity recognition, and scene understanding, etc (Lanaras et al., 2015; Eyal et al., 2019; Tu et al., 2020) . However, due to the insufficient prior information, inaccurate labels, complex scenes, and unbalanced samples, it is high-cost and sometimes infeasible to accurately detect different types of anomalies in HSI. Consequently, hyperspectral anomaly detection without any priors is a challenging task and is of great importance. Deep learning-based methods have powerful and unique advantages in modeling and characterizing complex data (Stanislaw et al., 2020) . A lot of research has appeared in the field of anomaly detection, which can be roughly divided into three categories: supervised, semi-supervised, and unsupervised. However, due to the difficulty of annotation and collection of label training, supervised methods are rarely applied (Grnitz et al., 2013; Raghavendra & Sanjay, 2019) . Semi-supervised work aims to break the dilemma between the number of samples and detection performance, but it still requires pure background training samples (Blanchar et al., 2010; Wu & Prasad, 2018) . On the one hand, unsupervised learning based hyperspectral anomaly detection has become a new trend (Schlegl et al., 2017; Zhang et al., 2019) . On the other hand, the detection performance is limited due to the lack of prior knowledge. Therefore, we propose an MCM-aware strategy to adaptively obtain reliable and stable pseudo-labeled prior information to alleviate these problems. Concretely, motivated by the observations mentioned above, we estimate the priors and model the background with multi-scale covariance matrices as the necessary preparation fed into the MTGAN model, which generates discriminative representations with second-order statistics in covariance 1

