Deep learning in remote sensing applications: A meta-analysis and review

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 152; pp. 166 - 177
• Main Authors: Ma, Lei, Liu, Yu, Zhang, Xueliang, Ye, Yuanxin, Yin, Gaofei, Johnson, Brian Alan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2019
• Subjects: algorithms; Deep learning (DL); image analysis; land use and land cover maps; LULC classification; meta-analysis; Object detection; Remote sensing; Scene classification; LULC classification; Scene classification; Deep learning (DL); Remote sensing; Object detection
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2019.04.015

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.