Application of PCA-CNN (Principal Component Analysis – Convolutional Neural Networks) Method on Sentinel-2 Image Classification for Land Cover Mapping

Ahmad Rizqi Pradana, Alfian Futuhul Hadi, Indarto

Land Cover, Sentinel-2, Deep Learning, PCA, CNN.

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.

Received: 09 Jul 2022, Received in revised form: 01 Aug 2022, Accepted: 07 Aug 2022, Available online: 15 Aug 2022

10.22161/ijaers.98.22