Anomaly Detection of Outlier Features from Spatio-temporal Databases of Landsat-8 Sensor, using Cloud Computing Platform
DOI:
https://doi.org/10.9734/bpi/ctmcs/v2/3616DKeywords:
Google earth engine, machine learning, algorithms, supervised learning methods, predictive analytics, data analytics, climate change, support vector machine, linear discriminant analysis, feature extraction, feature selectionAbstract
Recently there has been an explosion of research articles being published in scientific journals regarding machine learning and specialized algorithms, for feature identification, feature selection, and feature extraction studies. This article distinguishes the application of proof-of-concept from domains such as computer vision, remote sensing, image processing, and geospatial based databases technology. Using Landsat-8 sensor satellite imagery for rendering in multimedia and in scalable vector processing modes, the article lends credence to the fundamentals and principles in digital image analysis technique. With the application of user defined algorithm and scientific approach, the article in vigour details RSVM and DAFE scientific methods in cloud computing platform. It is proposed to bring about nuances of data analytics in distributed computing and parallel databases.
