Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers

Recent works have shown that passive capturing source detection methods based on Photo-Response-Non-Uniformity (PRNU) extraction are the most reliable ones in comparison with techniques that based on lens properties or compression artifacts. Some important issues in this field include: employing an effective method for extracting PRNU, calculating the similarity and categorizing videos according to source of camera. In this study, a comprehensive algorithm is proposed to compare and evaluate the performance of different source detection methods in terms of filters used and partitioning process applied for PRNU extraction coupled with SVM classifier. Moreover, in consideration of observations, a new method is proposed for sampling selection using SVM classifier. Furthermore, the capabilities of employing and combining the results of different color parts of videos are used instead of changing them to grayscale. The proposed algorithm is based on three essential steps: Firstly, fingerprint of each camera, which is regarded as reference PRNU, is calculated by extracting PRNU of blue-sky videos. Secondly, the PRNU similarities of sample videos with reference PRNU are measured by calculating cross correlation and Peak to Correlation Energy (PCE) metrics. Finally, the sample videos are classified based on calculated PCE with SVM classifier. Experimental results revealed that Zero-mean and Wiener filters have small influences on PRNU, thus they can be ignored. Experimental results also revealed that eliminating the partitioning step considerably increases the performance of detection success rate by 15%. Among SVM classifiers, “RBF” and “MLP” types have the best identification rate of 75%

Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers

Recent works have shown that passive capturing source detection methods based on Photo-Response-Non-Uniformity (PRNU) extraction are the most reliable ones in comparison with techniques that based on lens properties or compression artifacts. Some important issues in this field include: employing an effective method for extracting PRNU, calculating the similarity and categorizing videos according to source of camera. In this study, a comprehensive algorithm is proposed to compare and evaluate the performance of different source detection methods in terms of filters used and partitioning process applied for PRNU extraction coupled with SVM classifier. Moreover, in consideration of observations, a new method is proposed for sampling selection using SVM classifier. Furthermore, the capabilities of employing and combining the results of different color parts of videos are used instead of changing them to grayscale. The proposed algorithm is based on three essential steps: Firstly, fingerprint of each camera, which is regarded as reference PRNU, is calculated by extracting PRNU of blue-sky videos. Secondly, the PRNU similarities of sample videos with reference PRNU are measured by calculating cross correlation and Peak to Correlation Energy (PCE) metrics. Finally, the sample videos are classified based on calculated PCE with SVM classifier. Experimental results revealed that Zero-mean and Wiener filters have small influences on PRNU, thus they can be ignored. Experimental results also revealed that eliminating the partitioning step considerably increases the performance of detection success rate by 15%. Among SVM classifiers, “RBF” and “MLP” types have the best identification rate of 75%

Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions

The common methods of determining soil carbon (C), nitrogen (N) and their isotopic compositions (δ13C and δ15N) are expensive and time-consuming. Therefore, alternative low-cost and rapid methods are sought to address this issue. This study aimed to investigate the potential of hyperspectral image analysis to predict soil total carbon (TC), total nitrogen (TN), δ13C and δ15N. Hyperspectral images were captured from 96 ground soil samples using a laboratory-based visible to near-infrared (VNIR) hyperspectral camera in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) models were developed to correlate the values of TC, TN, δ13C and δ15N, obtained from isotope ratio mass spectrometry method, with their spectral reflectance. The developed models provided acceptable predictions with high coefficient of determination (R2c) and low root mean square error (RMSEc) of calibration set for TC (R2c = 0.82; RMSEc = 1.08%), TN (R2c = 0.87; RMSEc = 0.02%), δ13C (R2c = 0.82; RMSEc = 0.27‰) and δ15N (R2c = 0.90; RMSEc = 0.29‰). The prediction abilities of the models were then evaluated using the spectra of an external test set (24 samples). The models provided excellent predictions with high R2t and ratio of performance to deviation (RPD) of test set for TC (R2t = 0.76; RPD = 2.02), TN (R2t = 0.86; RPD = 2.08), δ13C (R2t = 0.80; RPD = 2.00) and δ15N (R2t = 0.81; RPD = 1.94). The results indicated that the laboratory-based hyperspectral image analysis has the potential to predict soil TC, TN, δ13C and δ15N

Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions

Hosseini Bai, S ORCiD: 0000-0001-8646-6423The common methods of determining soil carbon (C), nitrogen (N) and their isotopic compositions (δ13C and δ15N) are expensive and time-consuming. Therefore, alternative low-cost and rapid methods are sought to address this issue. This study aimed to investigate the potential of hyperspectral image analysis to predict soil total carbon (TC), total nitrogen (TN), δ13C and δ15N. Hyperspectral images were captured from 96 ground soil samples using a laboratory-based visible to near-infrared (VNIR) hyperspectral camera in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) models were developed to correlate the values of TC, TN, δ13C and δ15N, obtained from isotope ratio mass spectrometry method, with their spectral reflectance. The developed models provided acceptable predictions with high coefficient of determination (R2c) and low root mean square error (RMSEc) of calibration set for TC (R2c = 0.82; RMSEc = 1.08%), TN (R2c = 0.87; RMSEc = 0.02%), δ13C (R2c = 0.82; RMSEc = 0.27‰) and δ15N (R2c = 0.90; RMSEc = 0.29‰). The prediction abilities of the models were then evaluated using the spectra of an external test set (24 samples). The models provided excellent predictions with high R2t and ratio of performance to deviation (RPD) of test set for TC (R2t = 0.76; RPD = 2.02), TN (R2t = 0.86; RPD = 2.08), δ13C (R2t = 0.80; RPD = 2.00) and δ15N (R2t = 0.81; RPD = 1.94). The results indicated that the laboratory-based hyperspectral image analysis has the potential to predict soil TC, TN, δ13C and δ15N. © 2018 Elsevier B.V

The potential of hyperspectral images and partial least square regression for predicting total carbon, total nitrogen and their isotope composition in forest litterfall samples

Hosseini Bai, S ORCiD: 0000-0001-8646-6423Purpose: The main objective of this study was to examine the potential of using hyperspectral image analysis for prediction of total carbon (TC), total nitrogen (TN) and their isotope composition (δ13C and δ15N) in forest leaf litterfall samples. Materials and methods: Hyperspectral images were captured from ground litterfall samples of a natural forest in the spectral range of 400–1700 nm. A partial least-square regression model (PLSR) was used to correlate the relative reflectance spectra with TC, TN, δ13C and δ15N in the litterfall samples. The most important wavelengths were selected using β coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set. Results and discussion: The results showed that the data of TC and δ13C could not be fitted to the PLSR model, possibly due to small variations observed in the TC and δ13C data. The model, however, was fitted well to TN and δ15N. The cross-validation R2cv of the models for TN and δ15N were 0.74 and 0.67 with the RMSEcv of 0.53% and 1.07‰, respectively. The external validation R2ex of the prediction was 0.64 and 0.67, and the RMSEex was 0.53% and 1.19 ‰, for TN and δ15N, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and δ15N, showing that the models were reliable for the prediction of TN and δ15N in new forest leaf litterfall samples. Conclusions: The PLSR model was not successful in predicting TC and δ13C in forest leaf litterfall samples using hyperspectral data. The predictions of TN and δ15N values in the external litterfall samples were reliable, and PLSR can be used for future prediction. © 2017, Springer-Verlag GmbH Germany