Data Preprocessing
S. Mahmoudikhah; S. H. Zahiri; I. Behravan
Abstract
Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective ...
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Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective cognition procedures, which have robustness and low accuracy. Methods: In this research, a combination of classifiers has been used to improve the accuracy of sonar data classification in complex problems such as identifying marine targets. These classifiers each form their pattern on the data and store a model. Finally, a weighted vote is performed by the LA algorithm among these classifiers, and the classifier that gets the most votes is the classifier that has had the greatest impact on improving performance parameters.Results: The results of SVM, RF, DT, XGboost, ensemble method, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models have been compared with the proposed model. Considering that the objectives and databases are different, we benchmarked the average detection rate. In this comparison, Precision, Recall, F1_Score, and Accuracy parameters have been considered and investigated in order to show the superior performance of the proposed method with other methods.Conclusion: The results obtained with the analytical parameters of Precision, Recall, F1_Score, and Accuracy compared to the latest similar research have been examined and compared, and the values are 87.71%, 88.53%, 87.8%, and 87.4% respectively for each of These parameters are obtained in the proposed method.
Data Preprocessing
F. Tabib Mahmoudi; A. Karami
Abstract
Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, ...
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Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis.Methods: In this research, the pan-sharpened images from PCA, IHS, and Gram-Schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of Landsat-8 OLI sensor (medium scale resolution satellite) and WorldView-2 (high-resolution satellite). Quantitative analysis is performed on the pan-sharpened products based on the Per-Pixel Deviation (PPD) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. Moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content.Results: Quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated Pan-sharpened products. Gram-Schmidt transformation based pan-sharpening method has the least spectral deviations in both WorldView-2 and Landsat-8 satellite images. But, the amount of spectral, spatial and content-based quantitative measures of PCA and IHS are changing with various spatial resolutions.Conclusion: it can be said that Gram-Schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. The IHS pan-sharpening method has better performance than the PCA method in Landsat-8 OLI data. But, by increasing the spatial resolution of the data, PCA generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.
