Abstract: Satellite imageries are essentially a complex form of an image when subjected to critical analytical operation. The analytical process applied on remotely sensed satellite imageries are utilized for generating the land cover map. With an abundance of traditional techniques evolved to date, deep learning-based schemes are progressively gaining pace for identifying and classifying a terrestrial object in satellite images. However, different variants of deep learning approaches have different operations, and so are the consequences. At the same time, there is no reported literature to highlight the issues, trends, and effectiveness much on a generalized scale concerning segmentation. Therefore, this paper reviews some of the recent segmentation approaches using deep learning to contribute towards review findings in the form of research trends, research gaps, and essential learning outcomes. The paper offers a compact and distinct picture of deep learning approaches used to boost segmentation for satellite images.

Natya S, Ramya K and Seema Singh, “Insights on Deep Learning based Segmentation Schemes Towards Analyzing Satellite Imageries” International Journal of Advanced Computer Science and Applications(IJACSA), 12(11), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121114

@article{S2021,
title = {Insights on Deep Learning based Segmentation Schemes Towards Analyzing Satellite Imageries},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121114},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121114},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {11},
author = {Natya S and Ramya K and Seema Singh}
}