Review of machine learning methods for Big satellite Data classification

Abstract

With the appearance of free access to Big satellite data, the development of machine learning methods based on geospatial data, in particular satellite data, is becoming more and more relevant. In this paper, we consider and analyze the peculiarities of the basic machine learning methods and results of their application to the tasks of land cover classification based on high resolution satellite data. Special attention is paid to deep architectures, in particular, convolutional neural networks, which nowadays are the most powerful and precise method for visual pattern recognizing. We determine the main advantages of the deep learning methods over the traditional approaches to the classification tasks, that have been used over the last decades and based on expert knowledge to the features extraction from the input data.