Coupling of deep learning and remote sensing: a comprehensive systematic literature review

This study is conducted in accordance with a systematic literature review (SLR) protocol. SLR is tasked with finding publications, publishers, deep learning types, enhanced and adapted deep learning techniques, impacts, proactive approaches, key parameters, and applications in the field of remote sensing. It is also expected to identify current research directions, gaps, and unsolved issues in order to provide understanding and recommendations for future studies. The data is collected from important research papers published in recognized journals between the years 2015 and 2021, however, conference/seminar proceedings and other online resources are excluded to minimize unnecessary complications. Based on previously established exclusion, inclusion, and quality parameter criteria, a total of 122 primary studies are considered. The literature review overcomes a number of significant problems, including key variables taken into account by researchers in the remote sensing (RS) domain, various deep learning (DL) solutions proposed for RS analysis, various proactive strategies recommended in the literature to reduce risks linked to the RS domain, and various DL applications reported in the remote sensing domain. The results show that there is still a lack of structured information that enables DL to be employed for crucial applications in the field of remote sensing, despite substantial research and development of numerous DL algorithms. Furthermore, it is evident that DL approaches in the remote sensing domain have not been thoroughly exploited, thus demanding further research. The findings suggest that deep learning techniques need further investigations and the development of an authentic mechanism is essential for accurate results retrieved from remote sensing data. The proposed study would let scientists examine previous investigations into deep learning methods, which can then be utilized as support for further investigations.