Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections

2021 ◽  
Author(s):  
Idan Ben-Shabat ◽  
Lihi R. Zinger ◽  
Stanley R. Rotman
Keyword(s):  
Covariance Matrix ◽  
Hyperspectral Images ◽  
False Alarms

scholarly journals Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

2020 ◽  
Vol 10 (7) ◽  
pp. 2298
Author(s):  
Stig Uteng ◽  
Thomas Haugland Johansen ◽  
Jose Ignacio Zaballos ◽  
Samuel Ortega ◽  
Lasse Holmström ◽  
...  
Keyword(s):  
Food Quality ◽  
Principal Component ◽  
Real Data ◽  
Scale Space ◽  
Hyperspectral Images ◽  
False Alarms ◽  
Multiple Resolutions ◽  
Potentially Malignant ◽  
Malignant Changes ◽  
Small Change

Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies can be very useful in such situations since exploring the measurements in multiple resolutions can help identify even subtle changes. We extend a recently proposed scale-space methodology to a technique that successfully detects such small changes and at the same time keeps false alarms at a very low level. The potential of the novel methodology is first demonstrated with hyperspectral skin mole data artificially distorted to include a very small change. Our real data application considers hyperspectral images used for food quality detection. In these experiments the performance of the proposed method is either superior or on par with a standard approach such as principal component analysis.

The Multidimensional Algorithm of Cumulative Sums for Detecting Changes in Time Series Characteristics

Vestnik MEI ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 86-94
Author(s):  
Gennadiy F. Filaretov ◽  
◽  
Pavel S. Simonenkov ◽  
Keyword(s):  
Time Series ◽  
Covariance Matrix ◽  
Linear Transformation ◽  
Reference Data ◽  
Distribution Functions ◽  
Diagonal Form ◽  
False Alarms ◽  
Average Delay ◽  
Simultaneous Change ◽  
Monitoring Algorithm

The article presents a cumulative sum algorithm intended to detect a sudden step-like change in the probabilistic characteristics of a monitored time series when such a change (“disorder”) is associated with a simultaneous change in both the location characteristics and the dispersion characteristics of the corresponding distribution functions. In the general case of a multidimensional time series, the disorder is associated with a jump in the values of the mathematical expectation vector (the vector of means) and covariance matrix entries. To solve this problem, it is proposed to use a preliminary linear transformation of the time series values, as a result of which the covariance matrix is transformed to the unity form before disordering and to the diagonal form after disordering. The change in the vector of means is analyzed, and the main relations describing the considered detection algorithm are derived. It is noted that by using the above-mentioned linear transformation it is possible to simplify the obtaining of the reference data necessary for synthesizing the monitoring algorithm with the predetermined properties. As an example, a particular case of a one-dimensional time series and a disorder in the form of a simultaneous change in the mean and variance is considered. For this case, reference data obtained by applying the simulation method are given, using which it is possible to find the monitoring algorithm triggering threshold and estimate the average delay time of detecting the specified disorder from the given interval between false alarms. This study is a logical continuation and further development of the approach to construction of multidimensional algorithms for detecting disorders [1].

scholarly journals Modified hotelling’s T2 control charts using modified mahalanobis distance

2021 ◽  
Vol 11 (1) ◽  
pp. 284
Author(s):  
Firas Haddad
Keyword(s):  
Covariance Matrix ◽  
Mahalanobis Distance ◽  
Control Charts ◽  
Bootstrap Method ◽  
Arithmetic Mean ◽  
Probability Of Detection ◽  
False Alarms ◽  
The Individual ◽  
Mean Vector ◽  
Do So

This paper proposed new adjusted Hotelling’s T^2 control chart for individual observations. For this objective, bootstrap method for producing the individual observations were employed. To do so, both arithmetic mean vector and the covariance matrix in the traditional Hotelling’s T^2 chart were substituted by the trimmed mean vector and the covariance matrix of the robust scale estimators〖 Q〗_n, respectively which, in turn, its performance is carried out by simulated. In fact, the calculation of false alarms and the probability of detection outlier is used for determining the validity of this modified chart. The findings revealed a considerable significance in its performance.

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