Environmental Exploration and Monitoring of Vegetation Cover using Deep Convolutional Neural Network in Gombe State

Authors : Okere Chidiebere Emmanuel; Abdulrauf Abdulrasheed; Mustapha Abdulrahman Lawal; Ismail Zahraddeen Yakubu

Volume/Issue : Volume 8 - 2023, Issue 8 - August

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/2vdpatdf

DOI : https://doi.org/10.5281/zenodo.8337619

In the previous years, human assessments of satellite data were possible because to the relatively modest volume of images accessible; however, this is no longer the case. Additionally, traditional software like ARGIS, EDARS, ILWIS, and other time-consuming and ineffective tools are heavily used by environmental organizations in Nigeria, such as the National Centre for Remote Sensing. With today's large number of data, relevant information extraction from photos thus becomes a challenge. Differentiating between classes with comparable visual qualities is another problem, as seen when attempting to categorize a green pixel as grass, shrubs, or a tree. However, as seen in other computer vision fields, machine learning approaches have shown to be a strong answer in this case. We proposed the introduction of a novel three-dimensional based architecture that is specialized to multispectral pictures and addresses the majority of the challenging features of Deep Learning for Remote Sensing as a solution to the issues raised. This work's major goal is to create a deeper CNN architecture (U-Net) model that is efficient for semantically segmenting remote sensing imagery with additional multi-spectral feature classes. In comparison to traditional remote sensing software, our innovative 3D CNN architecture can analyze the spatial and spectral components simultaneously with true 3D convolutions. our results in better, faster, and more efficient segmentation.

Keywords : Computer Vision, Deep Learning, Semantic Segmentation of Satellite Imagery.


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31 - December - 2023

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