Deep Learning Analysis for Big Remote Sensing Image Classification

Abstract

Large data remote sensing has various special characteristics, including multi-source, multi-scale, large scale, dynamic and non-linear characteristics. Data set collections are so large and complex that it becomes difficult to process them using available database management tools or traditional data processing applications. In addition, traditional data processing techniques have different limitations in processing massive volumes of data, as the analysis of large data requires sophisticated algorithms based on machine learning and deep learning techniques to process the data in real time with great accuracy and efficiency. Therefore Deep learning methods are used in various domains such as speech recognition, image classifications, and learning methods in language processing. However, recent researches merged different deep learning techniques with hybrid learning-training mechanisms and processing data with high speed. In this paper we propose a hybrid approach for RS image classification combining a deep learning algorithm and an explanatory classification algorithm. We show how deep learning techniques can benefit to Big remote sensing. Through deep learning we seek to extract relevant features from images via a DL architecture. Then these characteristics are the entry points for the MLlib classification algorithm to understand the correlations that may exist between characteristics and classes. This architecture combines Spark RDD image coding to consider image’s local regions, pre-trained Vggnet and U-net for image segmentation and spark Machine Learning like random Forest and KNN to achieve labeling task.