Detecting land use changes using hybrid machine learning methods in the Australian tropical regions

Sedighkia, Mahdi, and Datta, Bithin (2022) Detecting land use changes using hybrid machine learning methods in the Australian tropical regions. Geojournal. (In Press)

[img]
Preview
PDF (Publisher Accepted Version) - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview
View at Publisher Website: https://doi.org/10.1007/s10708-022-10678...
 
85


Abstract

The present study evaluates the application of the hybrid machine learning methods to detect changes of land use with a focus on agricultural lands through remote sensing data processing. Two spectral images by Landsat 8 were applied to train and test the machine learning model. Feed forward neural network classifier was utilized as the machine learning model in which two evolutionary algorithms including particle swarm optimization and invasive weed optimization were applied for the training process. Moreover, three conventional training methods including Levenberg–Marquardt back propagation (LM), Scaled conjugate gradient backpropagation (SCG) and BFGS quasi-Newton backpropagation (BFG) were used for comparing the robustness and reliability of the evolutionary algorithms. Based on the results in the case study, evolutionary algorithms are not a reliable method for detecting changes through the remote sensing analysis in terms of accuracy and computational complexities. Either BFG or LM is the best method to detect the agricultural lands in the present study. BFG is slightly more robust than the LM method. However, LM might be preferred for applying in the projects due to low computational complexities.

Item ID: 75361
Item Type: Article (Research - C1)
ISSN: 1572-9893
Keywords: Particle swarm optimization, Invasive weed optimization, Back propagation, Neural network classifier, Remote sensing
Copyright Information: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Date Deposited: 29 Jun 2022 07:50
FoR Codes: 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing @ 100%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 70%
18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180603 Evaluation, allocation, and impacts of land use @ 30%
Downloads: Total: 85
Last 12 Months: 85
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page