RX Detector is recognized as the benchmark algorithm for image anomaly detection, however it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a highly dimensional covariance matrix and the inability to effectively include spatial awareness in its evaluation. In this work a novel graph-based solution to the ...

Supervised target detection and anomaly detection are widely used in various applications, depending upon the availability of target spectral signature. Basically, they are based on a similar linear process, which makes them highly correlated. In this paper, we propose a novel adjusted spectral matched filter (ASMF) for hyperspectral target detection, which aims to effectively improve target de...

A variety of detection statistics have been developed and applied to hyperspectral imagery (HSI). The Reed Xiaoli (RX) algorithm is a generalized likelihood ratio test (GLRT) that uses local estimates of the spectral mean and spectral covariance. It satisfies an optimality criterion if, locally, the spectral data have a multivariate normal probability distribution. Alternatively, the stochastic...

We propose a completely unsupervised pixel-wise anomaly detection (AD) method for hyperspectral images (HSIs). The proposed consists of three steps called data preparation, reconstruction, and detection. In the preparation step, we apply background purification to train deep network in an manner. reconstruction use different autoencoding adversarial (AEAN) models including 1-D-AEAN, 2-D-AEAN, 3...

The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in ava...

In this paper, a novel anomaly detection algorithm is proposed for hyperspectral imagery, which is the extended RX algorithm based on spectral dimension transformation and spatial filter (STSF-RX). Firstly, minimum noise fraction (MNF) transform is performed on the original hyperspectral images, by setting a SNR threshold, and obtains MNF transform matrices that the SNR of their corresponding b...

In hyperspectral anomaly detection, the dual-window-based detector is a widely used technique that employs two windows to capture nonstationary statistics of anomalies and background. However, its detection performance is usually sensitive to the choice of window sizes and suffers from inappropriate window settings. In this work, a decision-fusion approach is proposed to alleviate such sensitiv...

Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data. Such sensors provide fully registered high resolution spatial and spectral images that are invaluable in discriminating between man-made objects and natural clutter backgrounds. The price paid for this high resolution data is extremely large data sets, several hundred of Mbytes for a single scene, t...

With the arrival of km3 volume scale neutrino detectors the chances to detect the first astronomical sources of TeV neutrinos will be dramatically increased. While the theoretical estimates of the neutrino fluxes contain large uncertainties, we can formulate the conditions for the detectability of certain neutrino sources phenomenologically. In fact, since most galactic neutrino sources are tra...

Abstract Among the galactic sources that can be detected by KM3NeT detector, we will consider eHWC J1825-134 source recently HAWC telescope. Moreover, RX J1713.7-3946 and show prospects of detection in a fully hadronic lepto-hadronic scenario. Considering IceCube instead, focus on gamma-ray J1907+063 J2019+368. We found at 3 σ or more detector should within reach next decade. Finally, also 2HWC...