An analysis on deep learning approaches: addressing the challenges in remote sensing image retrieval

ABSTRACT

A considerable volume of high-resolution remote sensing (HRRS) data is generated with the intense space explorations happening globally. Remote sensing image retrieval (RSIR) is a fundamental task in the remote sensing domain, providing excellent opportunities for a broad spectrum of applications. Difficulty in describing the heterogeneous remote sensing image (RSI) content and the availability in huge volumes is challenging in many analyses and certainly needs many added processing challenges. Also, the popularity of using deep learning (DL) techniques is also increased in the remote sensing domain. The practice of using DL methods to strengthen the efficiency of RSIR frameworks has a very broad prospect. Thus, HRRS data brings new challenges for deep-learning-based RSIR (DL-RSIR) tasks, especially when the data is experienced as ‘big data.’ This article systematically reviews many existing works, concentrating on the advancements and current trends related to DL-RSIR. Also, it narrates the challenges incorporated with the RSIR tasks and how to utilize DL techniques and frameworks to address them. Almost all potential factors that could influence the DL-RSIR performance are analysed, and many evaluations are performed with a comparative analysis. The observations and recommendations presented can help researchers to bring new insights into designing DL-RSIR frameworks.

Disclosure statement

No potential conflict of interest was reported by the author(s).