Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification - Publikacja - MOST Wiedzy


Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification


Land Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various spectral feature bands are involved, but unexpectedly little consideration has been given to these characteristics in deep learning models. Due to the wide availability of RGB models in computer vision, this research mainly utilized RGB bands. Once the pre-processing is carried out for the images of the selected dataset, the hybrid feature extraction is performed using Haralick texture features, an oriented gradient histogram, a local Gabor binary pattern histogram sequence, and Harris Corner Detection to extract features from the images. After that, the Improved Mayfly Optimization (IMO) method is used to choose the optimal features. IMO-based feature selection algorithms have several advantages that include features such as a high learning rate and computational efficiency. After obtaining the optimal feature selection, the LULC classes are classified using a multi-class classifier known as the Multiplicative Long Short-Term Memory (mLSTM) network. The main functionality of the multiplicative LSTM classifier is to recall appropriate information for a comprehensive duration. In order to accomplish an improved result in LULC classification, a higher amount of remote sensing data should be processed. So, the simulation outcomes demonstrated that the proposed IMO-mLSTM efficiently classifies the LULC classes in terms of classification accuracy, recall, and precision. When compared with ConvNet and Alexnet, the proposed IMO-mLSTM method accomplished accuracies of 99.99% on Sat 4, 99.98% on Sat 6, and 98.52% on the Eurosat datasets.


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Opublikowano w:
Remote Sensing nr 14,
ISSN: 2072-4292
Rok wydania:
Opis bibliograficzny:
Stateczny A., Bolugallu S. M., Divakarachari P. B., Ganesan K., Muthu J. R.: Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification// Remote Sensing -Vol. 14,iss. 19 (2022), s.4837-
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs14194837
Politechnika Gdańska

wyświetlono 18 razy

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