Land Use and Land Cover Classification Using Recurrent Neural Networks with Shared Layered Architecture

Vignesh, T.; Kanimozhi, K.; Sathish, R.; Rai, Rajesh Kumar; Jeyavathana, R. Beaulah; Ezhumalai, P.

doi:10.1109/iccci54379.2022.9740839

Cited by 8 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, we could not locate an ANN-RF classifier combination in the land use and land cover classification field. This shows that the scientific gap that was studied in this study has not yet been filled (Vigneshl and Thyagharajan, 2014;Vignesh et al, 2016Vignesh et al, , 2022Vignesh and Thyagharajan, 2017;Thyagharajan and Vignesh, 2019).…”

Section: Discussionmentioning

confidence: 80%

“…This study covered the literature review about RF classifier from previous studies as follow: (Na et al, 2010;Wang et al, 2011Wang et al, , 2019Rodriguez-Galiano et al, 2012;Eisavi et al, 2015;He et al, 2015He et al, , 2022Ming et al, 2016;Sonobe et al, 2017;Nguyen et al, 2018;Thanh Noi and Kappas, 2018;Xu et al, 2018;Zhang et al, 2018;Abdullah et al, 2019;Gašparović et al, 2019;Márquez et al, 2019;Ge et al, 2020;Ghayour et al, 2021;Loukika et al, 2021;Rejith et al, 2021;Tan et al, 2021;Tassi et al, 2021;Vignesh et al, 2021;Behera et al, 2022;Girma et al, 2022;Huang and Wang, 2022;Karijadi and Chou, 2022;Matosak et al, 2022;Mwabumba et al, 2022;Sudhakar and Reddy, 2022).…”

Section: Rf Classifiermentioning

confidence: 99%

“…The limited literature indicates that more research on hybrid tactics is needed. It has been noted that the efforts to use mixed techniques are insufficient (Lo and Choi, 2004;Schepaschenko et al, 2011;Mahiny and Clarke, 2012;Singh et al, 2014;Vigneshl and Thyagharajan, 2014;Vignesh et al, 2016Vignesh et al, , 2022Sturari et al, 2017;Vignesh and Thyagharajan, 2017;Mishra et al, 2018;Thyagharajan and Vignesh, 2019;de Deus et al, 2021;Regasa et al, 2021;Wambugu et al, 2021;Behera et al, 2022) , .…”

Section: Multiple (Hybrid) Classifier Approachesmentioning

confidence: 99%

See 2 more Smart Citations

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

Alshari

Abdulkareem

Gawali

2023

Front. Artif. Intell.

View full text Add to dashboard Cite

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).

show abstract

Section: Discussionmentioning

confidence: 80%

Section: Rf Classifiermentioning

confidence: 99%

Section: Multiple (Hybrid) Classifier Approachesmentioning

confidence: 99%

See 1 more Smart Citation

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

Alshari

Abdulkareem

Gawali

2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…NWPU -Remote Sensing Image Scene Classification45, UC -Merced and Euro SAT: Sentinel -II data sets were analysed and findings were compared using various attributes and 3 distinct size remotesensing data -sets. T. M, P. P, R. S and A. T [4][5][6][7][8][9] proposed method for the land cover categorization and detecting changes of Guntur region with high-resolution satellite images with Landsat -7 data sets for the period 2013 and 2016. The change detection method is carried out utilizing the technique of UGG -19 with the CNN algorithm.…”

mentioning

confidence: 99%

Image Classification of Land Use Land Cover of Bengaluru City Using Convolutional Neural Network

R¹,

Sudaram²,

Rashmi³

2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

Developing countries like India is witnessing an increasing economic growth, rapid population in addition to industrialization leading to an increased rate of land use and cover. In order to better utilize the land and natural resource is essential to classify and analyse the land use and cover. Machine Learning and Deep Learning techniques are considered to be one of the effective and efficient ways for analysing and classifying the land use & cover. Here, in this paper, methodology for land use & cover classification – analysis of rural and urban regions of Bengaluru is been proposed. The proposed system’s main objective is to monitor the land cover changes of Bengaluru district including its rural and urban region for classifying the land cover into its exact classes. Classification algorithms such as SVM (Support Vector Machine), RF (Random Forest), KNN (K – Nearest Neighbor) and DT (Decision Tree) are used in the preprocessing of images and model created is tested using CNN. The Landsat datasets from usgs earth explorer is used. Performance evaluation of these algorithms are done based on their accuracy rates and efficiency. The proposed system shows that CNN classifies the land cover classes efficiently because of its highest accuracy and efficiency rates when compared with other algorithms.

show abstract

Detection of land cover usage from optimized learnable parameter artificial neural network (OLPANN) using multispectral images

Gowri,

Manjula

2024

Multimed Tools Appl

View full text Add to dashboard Cite

Land Use and Land Cover Classification Using Recurrent Neural Networks with Shared Layered Architecture

Cited by 8 publications

References 6 publications

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

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

Image Classification of Land Use Land Cover of Bengaluru City Using Convolutional Neural Network

Detection of land cover usage from optimized learnable parameter artificial neural network (OLPANN) using multispectral images

Contact Info

Product

Resources

About