“…Since then, a wide variety of anomaly detectors derived from RX/R-AD have been reported in the literature. For example, window-based local anomaly detectors, which implemented K or R using local windows [6][7], sliding windows [8], dual windows [9][10], multiple windows [11] and kernel anomaly detector [12], anomaly detection for unlabeled classification [13], real time processing of anomaly detection [14], guided filtering-based AD [15], spectral-spatial feature extraction-based AD [16], background separation-based AD [17], sparsity scoreestimation framework for AD [18]. Most recently, other approaches have been also developed such as deep learningbased anomaly detector, low rank and sparse matrix decomposition (LRaSMD) model-based anomaly detectors [19][20][21][22][23][24], low-rank and sparse representation [25][26][27], autoencoder [28][29][30], generative adversarial network (GAN) [31][32], game theory-based AD [33].…”