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Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

S. Vijayalakshmi*, S. Magesh Kumar

Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India

* Corresponding Author: S. Vijayalakshmi. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 2915-2931. https://doi.org/10.32604/iasc.2023.034165

Abstract

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

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APA Style
Vijayalakshmi, S., Kumar, S.M. (2023). Novel vegetation mapping through remote sensing images using deep meta fusion model. Intelligent Automation & Soft Computing, 36(3), 2915-2931. https://doi.org/10.32604/iasc.2023.034165
Vancouver Style
Vijayalakshmi S, Kumar SM. Novel vegetation mapping through remote sensing images using deep meta fusion model. Intell Automat Soft Comput . 2023;36(3):2915-2931 https://doi.org/10.32604/iasc.2023.034165
IEEE Style
S. Vijayalakshmi and S.M. Kumar, "Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model," Intell. Automat. Soft Comput. , vol. 36, no. 3, pp. 2915-2931. 2023. https://doi.org/10.32604/iasc.2023.034165



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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