期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：2
DOI：10.14569/IJACSA.2022.0130253
语种：English
出版社：Science and Information Society (SAI)
摘要：The advancement of remote sensing sensors acquired large amount of image data easily. Primary aspects of big data, such as volume, velocity, and variety, are represented in the acquired images. Furthermore, standard data processing approaches have different limits when dealing with such large amounts of data. As a result, good machine learning-based algorithms are required to process the data with higher accuracy and lower computational efficiency. Therefore, we propose ANOVA F-test based spectral feature selection method with a distributed implementation of this machine learning algorithm on Spark. Experimental results are obtained using the bench mark datasets acquired using AVIRIS and ROSIS sensors. The performance of Spark MLlib based supervised machine learning techniques are evaluated using the criteria viz., accuracy, specificity, sensitivity, F1-score and execution time. Added to that, we compared the execution time between distributed processing and processing with single processor. The results reveal that the proposed strategy significantly cuts down on analytical time while maintaining classification accuracy.
