Title: Integration of statistical parameters-based colour-texture descriptors for radar remote sensing image retrieval applications

Authors: Naushad Varish; Sambidi Rohan Reddy; Nadimpalli Gautham Sashi Varma; Priyanka Singh

Addresses: Department of Computer Science and Engineering, GITAM (Deemed to be University), Hyderabad – 502329, Telangana, India ' Department of Computer Science and Engineering, SRM University, Amaravati – 522502, Andhra Pradesh, India ' Department of Computer Science and Engineering, SRM University, Amaravati – 522502, Andhra Pradesh, India ' Department of Computer Science and Engineering, SRM University, Amaravati – 522502, Andhra Pradesh, India

Abstract: In this paper, a novel image retrieval method based on colour-texture contents for radar remote sensing applications is proposed, where global properties-based colour contents are extracted from different numbers of groups of histograms of colour image planes, and local properties-based texture contents have been derived from block level GLCM of an image plane. The integration of colour-texture contents represents the low dimensional feature which reduces overall computational overhead and increases the retrieval speed. To give importance to the feature components, suitable weights are imposed to both colour-texture contents appropriately. The obtained feature information describes the radar image effectively and also similarity measures play a significant role for better performance. This work compares eight similarity metrics to select the best one in the retrieval process. To validate the suggested method, experiments on two image datasets are performed and good retrieval results have been attained with rich colour-texture contents.

Keywords: remote sensing image retrieval; statistical parameters; grey-level co-occurrence matrix; GLCM; feature descriptors; min-max; similarity measures.

DOI: 10.1504/IJCSE.2024.137289

International Journal of Computational Science and Engineering, 2024 Vol.27 No.2, pp.204 - 218

Accepted: 23 Dec 2022
Published online: 11 Mar 2024 *

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