Infrared image recognition by means of FLIR cameras (forward-looking infrared) is one of the elements of the recognition of the maritime situation and it supports in many situations the creation of so-called maritime picture. This paper presents results of two FLIR image classifiers research. The first part presents the use of SVM (Support Vector Machine) to classify images of maritime objects, while the second part presents the classifier using the time series comparison method DTW (data time warping). The SVM network uses to perform the multi-class classification the oneagainst-all method. Both classifiers use the histograms of vertical projection of pre-processed FLIR images as input data (for training and testing). These histograms are created as a result of FLIR color images processing, including, among others, transformation of color images into grayscale images, grayscale images segmentation using the Otsu algorithm with a possible manual correction, rescaling, centering and leveling. In the further part of the work a method of determining the basic belief assignment is proposed for both SVM and DTW classifiers. In the final part of the paper test results of the both classification methods and their fusion by the Dempster-Shafer method for a set of maritime objects FLIR images registered in the Baltic Sea are presented.
This paper presents a method of recognition of maritime objects based on FLIR (forward looking infra-red) sensor images using two methods: Principal Component Analysis (PCA) and Dynamic Time Warping (DTW). A combination of the Principal Component Analysis PCA with the eigenimages analysis method reduces the dimensionality of the recognition problem. DTW method finds the shortest distance between two time series allowing a transformation of time for both compared series. In the presented maritime objects FLIR images classifier the DTW method is used to compare the vertical brightness projection histograms of silhouettes for the recognized object and the object pattern. To determine the silhouette of a maritime object the Otsu thresholding algorithm is used. The paper describes the eigenimages method, the DTW method of comparing time series and the data fusion method combining conclusions both classifiers. In the final part of the paper are presented preliminary test results of the classification method for a set of maritime objects FLIR images registered in the Baltic Sea.
Infrared image recognition by means of FLIR cameras (forward-looking infrared) is one of the elements of the recognition of the maritime situation and it supports in many situations the creation of so-called maritime picture. This paper presents results of two FLIR image classifiers research. The first part presents the use of neural networks to classify images of maritime objects, while the second part presents the classifier using the time series comparison method called the DTW (data time warping). The neural network is a three-layered artificial neural network (feed forward). Both classifiers use the histograms of vertical projection of pre-processed FLIR images as input data. These histograms are created as a result of FLIR color images processing, including, among others, transformation of color images into grayscale images, grayscale images segmentation using the Otsu algorithm, rescaling, centering and leveling. In the final part of the paper preliminary test results of the both classification methods for a set of maritime objects FLIR images registered in the Baltic Sea are presented.
KEYWORDS: Sensors, Information fusion, Monte Carlo methods, Electronic support measures, Signal detection, Electronic signals intelligence, Radar, Databases, Radar signal processing, Intelligent sensors
This paper presents a method of conflicts removal in fusion of identification (attribute) information provided by ELINT – ESM sensors (Electronic Intelligence – Electronic Support Measures). In the first section, the basic taxonomy of attribute identification is adopted in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft). These standards provide the following basic values of the attribute identifications: FRIEND, HOSTILE, NEUTRAL, UNKNOWN and additional values: ASSUMED FRIEND and SUSPECT. The last values can be interpreted as a conjunction of basic values. The basis of theoretical considerations is the Dezert-Smarandache theory (DSmT) of inference. The paper presents and practically uses for combining identification information from different ELINT – ESM sensors four information fusion rules proposed by the DSmT - the Proportional Conflict Redistribution rules (PCR1, PCR3,Pcr4 and PCR5).In the next section, rules of determining attribute information by ESM sensor equipped with the data base of radar emitters are presented. It was proposed that each signal vector sent by the ELINTESM sensor contained an extension specifying a randomized identification declaration (hypothesis). This declaration specifies the reliability of the identification information - basic belief assignment (bba) for the identification information set. Results of the PCR rules of sensor information combining for different scenarios of radio-electronic situation (deterministic and Monte Carlo) are presented in the final part of the paper. At the end of the paper conclusions are given. They confirm the legitimacy of the use of the Dezert-Smarandache theory into information fusion and its proportional conflict redistribution rules for ELINT-ESM sensors.
KEYWORDS: Sensors, Information fusion, Electronic support measures, Electronic signals intelligence, Databases, Radar, Signal detection, Signal processing
This paper presents a method of fusion of identification (attribute) information provided by ELINT – ESM sensors (Electronic Intelligence – Electronic Support Measures). In the first section the basic taxonomy of attribute identification in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft) is adopted. These standards provide the following basic values of the attributes of identification: FRIEND, HOSTILE, NEUTRAL, UNKNOWN and additional values: ASSUMED FRIEND and SUSPECT. The last values can be interpreted as a conjunction of basic values. The basis of theoretical considerations is the Dezert-Smarandache theory (DSmT) of inference. This paper presents and practically uses combining identification information from different ELINT – ESM sensors one of the information fusion rules proposed by the DSmT - the Proportional Conflict Redistribution #5 rule (PCR5). In the next section rules of determining attribute information by ESM sensor equipped with the data base of radar emitters are presented. It was proposed that each signal vector sent by the ELINT-ESM sensor contained an extension specifying a randomized identification declaration (hypothesis). This declaration specifies the reliability of the identification information - basic belief assignment (bba) for the identification information set. This paper presents a method of determining this belief assignment based on the distance between recognized signal features, vectors and centers of clusters grouping emitter patterns in the pattern data base. Results of the PCR5 rule of sensor information combining for two scenarios are presented in the final part of this paper. Conclusions are given at the end of this paper. They confirm the legitimacy of the use of the Dezert-Smarandache theory into information fusion for ESM sensors.
This paper presents a method of recognition of maritime objects based on their images made by infrared sensors (FLIR – forward looking infra-red) using the time series comparison DTW method (DTW - Dynamic Time Warping). The DTW method allows to find the smallest distance between two time series when the run of time one of the series has been deformed (stretched or compressed). In the presented classifier of maritime objects images the DTW method is used to compare the combined horizontal and vertical brightness histograms for a recognized object and pattern objects. The DTW method allows to compare the histograms of objects whose FLIR images were taken at different angles. To determine the silhouette of a maritime object the Otsu segmentation algorithm is used in this paper. The paper describes the Otsu threshold method, the method of comparing time series DTW and the method of constructing combined histograms of maritime objects silhouettes. The final part of the paper presents the results of research on the developed method of maritime objects classification using a set of FLIR images registered in the Baltic Sea.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.