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

被引:22
|
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
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
收藏
页数:15
相关论文
共 50 条
  • [1] Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support
    Delogu, Gabriele
    Caputi, Eros
    Perretta, Miriam
    Ripa, Maria Nicolina
    Boccia, Lorenzo
    SUSTAINABILITY, 2023, 15 (18)
  • [2] Temporal generalization of an artificial neural network for land use/land cover classification
    Tolentino, Franciele M.
    Galo, Maria de Lourdes B. T.
    Christovam, Luiz E.
    Coladello, Leandro F.
    EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS IX, 2018, 10790
  • [3] Land Use Land Cover Change Detection and Prediction Using Ann for Varanasi City
    Dey, Bratati
    Sharma, Poonam
    Naaz, Juveriya
    SSRN,
  • [4] Land cover and land use classification using TER-UTI
    Gay, C
    Porchier, JC
    AGRICULTURAL STATISTICS 2000, PROCEEDINGS: AN INTERNATIONAL CONFERENCE ON AGRICULTURAL STATISTICS, 1998, : 193 - 201
  • [5] The use of backpropagating artificial neural networks in land cover classification
    Kavzoglu, T
    Mather, PM
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2003, 24 (23) : 4907 - 4938
  • [6] Interpretable Approaches for Land Use and Land Cover Classification
    Osias, Ana C. F.
    Schaefer, Mariana A. R.
    Veloso, Gustavo V.
    de Oliveira, Hugo N.
    Reis, Julio C. S.
    PROCEEDINGS OF THE 20TH BRAZILIAN SYMPOSIUM ON INFORMATIONS SYSTEMS, SBSI 2024, 2024,
  • [7] Land use - Land cover classification for the island of Cephalonia
    Vassilopoulos, A
    Gournelos, T
    Evelpidou, N
    PROTECTION AND RESTORATION OF THE ENVIRONMENT VI, VOLS I - III, PROCEEDINGS, 2002, : 1761 - 1768
  • [8] THE USE OF LAND COVER CHANGE LIKELIHOOD FOR IMPROVING LAND COVER CLASSIFICATION
    Reis, Mariane S.
    Sant'Anna, Sidnei J. S.
    Dutra, Luciano V.
    Escada, Maria Isabel S.
    Pantaleao, Eliana
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 3222 - 3225
  • [9] HYPERSPECTRAL DATA FOR LAND USE/LAND COVER CLASSIFICATION
    Vijayan, Divya V.
    Shankar, G. Ravi
    Shankar, T. Ravi
    ISPRS TECHNICAL COMMISSION VIII SYMPOSIUM, 2014, 40-8 : 991 - 995
  • [10] A Hybrid Approach for Land Use/Land Cover Classification
    Tang, Yanbing
    Pannell, Clifton W.
    GISCIENCE & REMOTE SENSING, 2009, 46 (04) : 365 - 387