Application of PCA-CNN (Principal Component Analysis – Convolutional Neural Networks) Method on Sentinel-2 Image Classification for Land Cover Mapping
Date
2022-08-01Author
PRADANA, Ahmad Rizqi
HADI, Alfian Futuhul
INDARTO, Indarto
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Show full item recordAbstract
Land cover information based on remote sensing imagery is
effective information for land use management. The use of Sentinel-2
imagery is considered to be able to provide better information on land
cover because it has a spatial accuracy of 10 meters. Convolutional Neural
Networks is one of the deep learning methods that can be used for image
interpretation in order to obtain image classification results which will later
obtain information about land cover. PCA-CNN (Principal Component
Analysis-Convolutional Neural Network) is a development method of the
Convolutional Neural Network method which gives special treatment to
the dimension reduction process in the input data. The dimension reduction
process is carried out by utilizing the PCA method so that the data
processing process becomes faster without losing important information so
that better method performance is obtained. The PCA-CNN method is
implemented on a dataset of the Situbondo district which is classified into
five land cover classes. The results of the PCA-CNN method have an
Overall Accuracy of 94.4% and Kappa Indeks 0,92 with 100 epochs of
repeated experiments.
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- LSP-Jurnal Ilmiah Dosen [7292]