Classification of land use/land cover using artificial intelligence (ANN-RF)

被引:26
作者
Alshari, Eman A. [1 ,2 ]
Abdulkareem, Mohammed B. [2 ]
Gawali, Bharti W. [3 ]
机构
[1] Thamar Univ, Dept Comp Sci & Informat Technol, Dhamar, Yemen
[2] Al Maarif Univ Coll, Dept Comp Engn Tech, Ramadi, Iraq
[3] Dr Babasaheb Ambedkar Marathwada Univ, Dept Comp Sci & Informat Technol, Aurangabad, India
来源
FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2023年 / 5卷
关键词
artificial neural networks (ANN); random forest (RF); Sana'a city; neural-based; object-based; CHALLENGES; LANDSAT-7; PLUS;
D O I
10.3389/frai.2022.964279
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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).
引用
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页数:15
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