Title: Integrating Thepade SBTC and Niblack thresholding features for identification of land usage from aerial images using ensemble of machine learning algorithms

Authors: Sudeep D. Thepade; Sandeep Chauhan

Addresses: Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India ' Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India

Abstract: The aerial images taken by satellites and drones are used to identify different types of land utilisation. Land use identification (LUI) is attempted using several machine learning (ML) algorithms, which are trained with the aerial image features extracted using global or local content. The work here presents a fusion of globally extracted Thepade SBTC (TSBTC) features and local extracted Niblack thresholding features for LUI. Extraction of features from an aerial image using TSBTC is done with ten variations from 2-ary to 11-ary. Nine ML classifiers and ensembles are considered. The UC-Merced-dataset, containing 2,100 photos split over 21 different land-use-types, is used for experimentation. The performance metrics alias F-measure, accuracy and MCC performance are used. The fusion of TSBTC and Niblack has given a better LUI. In TSBTC variations, TSBTC 11-ary has given better LUI. The ensembles have given better LUI. The 'IBK + RF + SL' ensemble performs better.

Keywords: land use identification; LUI; Niblack; Thepade sorted BTC; Thepade sorted BTC N-ary; aerial image.

DOI: 10.1504/IJCVR.2024.141811

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.6, pp.615 - 630

Received: 02 Feb 2023
Accepted: 15 Mar 2023
Published online: 02 Oct 2024 *

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