A review of remote sensing image segmentation by deep learning methods

ABSTRACT

Remote sensing (RS) images enable high-resolution information collection from complex ground objects and are increasingly utilized in the earth observation research. Recently, RS technologies are continuously enhanced by various characterized platforms and sensors. Simultaneously, artificial intelligence vision algorithms are also developing vigorously and playing a significant role in RS image analysis. In particular, aiming to divide images into different ground elements with specific semantic labels, RS image segmentation could realize the visual acquisition and interpretation. As one of the pioneering methods with the advantages of deep feature extraction ability, deep learning (DL) algorithms have been exploited and proved to be highly beneficial for precise segmentation in recent years. In this paper, a comprehensive review is performed on remote sensing survey systems and various kinds of specially designed deep learning architectures. Meanwhile, DL-based segmentation methods applied on four domains are also illustrated, including geography, precision agriculture, hydrology, and environmental protection issues. In the end, the existing challenges and promising research directions in RS image segmentation are discussed. It is envisioned that this review is able to provide a comprehensive and technical reference, deployment and successful exploitation of DL empowered RS image segmentation approaches.

KEYWORDS:

• Remote sensing
• deep learning
• image segmentation

Disclosure statement

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

Data availability statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Additional information

Funding

This work was supported by the Natural Science Foundation of China under Grant 42201386, in part by the Fundamental Research Funds for the Central Universities and the Youth Teacher International Exchange and Growth Program of USTB (QNXM20220033), and Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities: FRF-IDRY-22-018).