Mangaraj, S. (2021) Classification of land use/land cover using artificial intelligence (ANN-RF). Frontiers in Agronomy, 3. pp. 128-134. ISSN 2581-8627
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Abstract
Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).
Item Type: | Article |
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Subjects: | Open Asian Library > Agricultural and Food Science |
Depositing User: | Unnamed user with email support@openasianlibrary.com |
Date Deposited: | 06 Jan 2023 10:48 |
Last Modified: | 04 Oct 2024 05:47 |
URI: | http://publications.eprintglobalarchived.com/id/eprint/120 |