Content-Based remote sensing image retrieval method using adaptive tetrolet transform based GLCM features

Article type: Research Article

Authors: Varish, Naushad^a | Hasan, Mohammad Kamrul^{g; *} | Khan, Asif^{b; *} | Zamani, Abu Taha^c | Ayyasamy, Vadivel^d | Islam, Shayla^g | Alam, Rizwan^e

Affiliations: [a] Department of Computer Science and Engineering, GITAM (Deemed to be University), Hyderabad, Telangana, India | [b] Department of Computer Application, Integral University, Lucknow, Uttar Pradesh, India | [c] Department of Computer Science, Northern Border University, Arar, Kingdom of Saudi Arabia | [d] Computer Science and Engineering, GITAM School of Technology, Bengaluru Campus, Karnataka, India | [e] United world School of Computational Intelligence, Karnavati University, Gandhinagar, Gujarat, India | [f] Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia | [g] Institute of Computer Science and Digital Innovation, UCSI University Malaysia

Correspondence: [*] Corresponding author. Mohammad Kamrul Hasan, E-mail: mkhasan@ukm.edu.my and Asif Khan, E-mail: asif05amu@gmail.com.

Abstract: This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods.

Keywords: Radar remote sensing image retrieval, adaptive tetrolet transform, gray level Co-occurrence matrix, prediction learning, statistical parameters

DOI: 10.3233/JIFS-224083

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9627-9650, 2023