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A Hybrid Optimal Technique for Road Extraction Using Entropy Rate Super-Pixel Segmentation and Probabilistic Neural Networks by D. Subhashini; V. B. S. Srilatha Indira Dutt is a nonfiction available to read on EtoBox.

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Road extraction is the leading type of application in the area of remote sensing image systems. The major challenges in road network extraction are classified under two categories: intensity and width. The majority of relevant research has focused on machine learning-based methods; however, they have not been able to achieve the highest level of extraction accuracy. Thus, research work is implemented with the advanced deep learning-based probabilistic neural networks classification mechanism to overcome this. Initially, the entropy rate super-pixel segmentation approach is used to efficiently detect the lanes of road, respectively. Finally, to achieve this, probabilistic neural networks were developed for the classification of the road and non-road classes. Texture features, discrete wavelet transform-based low-level features, and statistical color features each have their own co-occurrence matrix at the gray level. Finally, road object analysis operation was performed on the classified outputs; simulation analysis shows that the proposed method shows better qualitative and quantitative analysis. Introduction In the study of natural resources and the environment, remotely sensed da

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Author

D. Subhashini; V. B. S. Srilatha Indira Dutt

Publisher

Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd

Published

2023

Language

EN

ISBN

9789811984969

Category

nonfiction

Subjects

Computer Science, Engineering, Technology