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Publication: IIP Proceedings Year: 2022, Month: November Page No: 131-146, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023AA7D34DFE398010.pdf
EXTRACTION OF MAN-MADE OBJECT FROM REMOTE SENSING IMAGES USING GABOR ENERGY FEATURES AND NEURAL NETWORKS
Area/Stream: Artificial Intelligence, Authors: Md. Abdul Alim Sheikh Keywords: Remote Sensing Image, Man-made Object Extraction, Gabor Wavelets, Probabilistic Neural network Book Name /series: Futuristic Trends in Artificial Intelligence ,Volume 2, Book 16, Chapter 13Publication: IIP Proceedings Year: 2022, Month: November Page No: 131-146, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023AA7D34DFE398010.pdf
Abstract:
This chapter presents a novel approach for man-made object extraction in Remote Sensing (RS) images. This paper focuses on the design and implementation of a system that allows a user to extract multiple objects such as buildings or roads from an input image without much user intervention. The framework includes five main stages: 1) Pre-processing Stage. 2) Extraction of Local energy features using edge information and Gabor filter followed by down sampling to reduce the redundant information. 3) Further reduction of the size of feature vectors using Wavelet decomposition. 4) Classification and recognition of man-made structures using Probabilistic Neural Network (PNN) 5) NDVI based postclassification refinement. Experiments are carried out on a dataset of 200 RS images. The proposed framework yields Overall Accuracy (OA) of 93%. Experimental results validate the effective performance of the suggested method for manmade objects extraction from RS images. Compared with other methods; the proposed framework exhibits significantly improved accuracy results and computationally much more efficient. Most notably, it has a much smaller input size, which makes it more feasible in practical applications.
Cite this: Md. Abdul Alim Sheikh,"EXTRACTION OF MAN-MADE OBJECT FROM REMOTE SENSING IMAGES USING GABOR ENERGY FEATURES AND NEURAL NETWORKS", Futuristic Trends in Artificial Intelligence ,Volume 2, Book 16, Chapter 13, November, 2022, 131-146, 978-93-95632-70-6, https://rsquarel.org/assets/docupload/rsl2023AA7D34DFE398010.pdf
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