Analysis of deep learning architecture for patch-based land cover classification

Fahmi, Hisyam ORCID: https://orcid.org/0000-0002-2665-1536 and Sari, Wina Permana ORCID: https://orcid.org/0000-0003-3289-2640 (2022) Analysis of deep learning architecture for patch-based land cover classification. Presented at The 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 13 Dec 2022, Yogyakarta.

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Abstract

In recent years, the usage of computer vision methods for mapping land cover and land use has increased. Patch-based change detection may produce much superior results than pixel-based change detection and generate accurate change maps. Convolutional Neural Network (CNN) is an excellent option for remote sensing applications using hyperspectral data. The training of models using deep learning techniques takes a lot of time. This research aims to determine the most efficient deep learning model for patch-based land-cover classification by examining and comparing three CNN architectural models for land cover classification: LeNet-5, VGG-16, and ResNet-50. EuroSAT data derived from Sentinel-2A remote sensing imagery are utilized in this work. Comparing the three CNN architectures indicates that ResNet-50 has the highest validation accuracy, with a testing accuracy of 0.877, and a training time that is neither too quick nor too slow. The LeNet-5 model has the quickest training time but the lowest accuracy. VGG-16 has the longest training period yet has the highest test score of 0.878.

Item Type:	Conference (Paper)
Keywords:	training; deep learning; electrical engineering; computer architecture; convolutional neural networks; information technology; testing
Subjects:	17 PSYCHOLOGY AND COGNITIVE SCIENCES > 1702 Cognitive Sciences > 170203 Knowledge Representation and Machine Learning
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Hisyam Fahmi
Date Deposited:	13 Apr 2023 10:41

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