Deep learning techniques for remote sensing

Abstract

Due to the wide swath and acceptable cost, remote sensing (RS) techniques have been widely applied in extensive applications, such as land cover land use (LCLU), flood detection, urbanization monitoring. A number of airborne and space-borne missions are conducted to acquire remote sensing data - ALOS, TerraSAR-X, Sentinel-1, Sentinel-2, GEDI, UAVSAR, Landsat, to name a few. They carried different types of sensors that differ from each other in terms of resolution, penetration ability, and imaging mechanism, thus are suitable for different applications scenarios. Accordingly, it calls for specifically designed models for different types of data.With the accumulation of years of the vast amount of data, how to effectively use them especially in an automatic manner to serve for practical applications becomes a challenge. Deep learning (DL), which has achieved great success in other tasks in the computer vision field, is employed as a powerful tool for dealing with remote sensing data [1-6]. Previous studies reviewed the basic deep learning models and their applications in remote sensing data regarding either the data types [2,4] or the task types [1,3,5,6]. They mainly align the remote sensing tasks with the computer vision tasks. In this chapter, however, we revisit several hot topics that come from the fields of target recognition, land cover and land use (LCLU), weather forecasting, and forest monitoring, introduce how various deep learning models are employed and fitted into these specific tasks.