Optimal feature space for semantic image segmentation

Anishchenko, Sergey; Petrushan, Mikhail

doi:10.1134/s1054661814040026

Cited by 1 publication

(2 citation statements)

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“…Any deep learning model may need a huge set of training images due to the number of classes and complications of the problem [142]. Moreover, the utilization of deep learning is more complicated while considering the expensive and additional remote-sensing data collection [143].…”

Section: Research Gaps and Challengesmentioning

confidence: 99%

“…Similarly, the label dataset can be created with the feature of pretrained models. From the meta-analysis results, the deep learning provides enhanced efficiency and shows the superior performance when compared to conventional approaches [143]. Many challenges of deep learning-adopted techniques have been solved and reduced in recent decades, which have to increase the performance.…”

Section: Research Gaps and Challengesmentioning

confidence: 99%

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Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

Anilkumar

Venugopal

2022

Security and Communication Networks

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Semantic segmentation is a significant research topic for decades and has been employed in several applications. In recent years, semantic segmentation has been focused on different deep learning approaches in the area of computer vision, which has aimed for getting superior efficiency while analyzing the aerial and remote-sensing images. The main aim of this review is to provide a clear algorithmic categorization and analysis of the diverse contribution of semantic segmentation of aerial images and expects to give the comprehensive details associated with the recent developments. In addition, the emerged deep learning methods demonstrated much improved performance measures on several public datasets and incredible efforts have been dedicated to advancing pixel-level accuracy. Hence, the analysis on diverse datasets of each contribution is studied, and also, the best performance measures achieved by the existing semantic segmentation models are evaluated. Thus, this survey can facilitate researchers in understanding the development of semantic segmentation in a shorter time, simplify understanding of its latest advancements, research gaps, and challenges to be used as a reference for developing the new semantic image segmentation models in the future.

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Section: Research Gaps and Challengesmentioning

confidence: 99%

Section: Research Gaps and Challengesmentioning

confidence: 99%