TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification using semantic feature and machine learning: Land-use case application

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification using semantic feature and machine learning: Land-use case application

Convolutional neural networks
Feature extraction
Land-use classification
Machine learning

Land cover classification has interested recent works especially for
deforestation, urban are monitoring and agricultural land use. Traditional

classification approaches have limited accuracy especially for non-
heterogeneous land cover. Thus, using machine may improve the classification

accuracy. The presented paper deals with the land-use scene recognition on
very high-resolution remote sensing imagery. We proposed a new framework
based on semantic features, handcrafted features and machine learning
classifiers decisions. The method starts by semantic feature extraction using a
convolutional neural network. Handcraft features are also extracted based on
color and multi-resolution characteristics. Then, the classification stage is
processed by three learning machine algorithms. The final classification result
performed by majority vote algorithm. The idea behind is to take advantages
from semantic features and handcrafted features. The second scope is to use
the decision fusion to enhance the classification result. Experimentation results
show that the proposed method provides good accuracy and trustable tool for
land use image identification.

Hela Elmannai, Abeer Dhafer Al-Garni

http://journal.uad.ac.id/index.php/TELKOMNIKA

Feb 26, 2021

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