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Development of Adaptive Time-Weighted Dynamic Time Warping for Time Series Vegetation Classification Using Satellite Images in Solapur District

Development of Adaptive Time-Weighted Dynamic Time Warping for Time Series Vegetation... The global seasonal change and continued rapid growth have maximized the need to assess the urban dwellers’ depend on vegetation for their lives, and also in the urban ecosystem resources. The conventional outcomes devoted to cropland mapping, with the help of high-quality remote sensing data’s. This paper is to investigate and develop a new methodology that pertains to time series analysis for classifying the type of vegetation in a farm area of Ujani Dam located in Solapur District, Maharastra. The proposed model develops a novel adaptive time-weighted dynamic time warping (ATWDTW) for the time series analysis using the satellite images. The gathered satellite images from the farm are processed initially and subjected to analysis by ATWDTW. The TWDTW concept is optimally tuned by the new hybrid meta-heuristic algorithm termed moth flame-based bird swarm optimization (MF-BSA) for enhancing the classification performance. Regarding the false omission rate of the proposed MF-BSA-ATWDTW model attains 5.56% and 29.9% lower than SVM and K-means respectively. From the analysis, it is possible to get a deep insight into the vegetation to be done in each year, and the comparative analysis proves that the proposed model is further adaptable for experimental use in relating and explaining environmental and ecological time-series information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Computer Journal Oxford University Press

Development of Adaptive Time-Weighted Dynamic Time Warping for Time Series Vegetation Classification Using Satellite Images in Solapur District

Kumawat, Manisha; Khaparde, Arti
The Computer Journal , Volume 66 (8): 18 – May 15, 2022
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References (37)

Publisher
Oxford University Press
Copyright
© The British Computer Society 2022. All rights reserved. For permissions, please e-mail: [email protected]
ISSN
0010-4620
eISSN
1460-2067
DOI
10.1093/comjnl/bxac057
Publisher site
See Article on Publisher Site

Abstract

The global seasonal change and continued rapid growth have maximized the need to assess the urban dwellers’ depend on vegetation for their lives, and also in the urban ecosystem resources. The conventional outcomes devoted to cropland mapping, with the help of high-quality remote sensing data’s. This paper is to investigate and develop a new methodology that pertains to time series analysis for classifying the type of vegetation in a farm area of Ujani Dam located in Solapur District, Maharastra. The proposed model develops a novel adaptive time-weighted dynamic time warping (ATWDTW) for the time series analysis using the satellite images. The gathered satellite images from the farm are processed initially and subjected to analysis by ATWDTW. The TWDTW concept is optimally tuned by the new hybrid meta-heuristic algorithm termed moth flame-based bird swarm optimization (MF-BSA) for enhancing the classification performance. Regarding the false omission rate of the proposed MF-BSA-ATWDTW model attains 5.56% and 29.9% lower than SVM and K-means respectively. From the analysis, it is possible to get a deep insight into the vegetation to be done in each year, and the comparative analysis proves that the proposed model is further adaptable for experimental use in relating and explaining environmental and ecological time-series information.

Journal

The Computer Journal – Oxford University Press

Published: May 15, 2022

Keywords: time series vegetation classification; adaptive time-weighted dynamic time warping; moth flame-based bird swarm optimization; satellite images; Solapur district

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