Open AccessLand Use and Land Cover Classification Using CNN, SVM, and Channel Squeeze & Spatial Excitation Block
Author(s) -
Herdito Ibnu Dewangkoro,
A. M. Arymurthy
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/704/1/012048
Subject(s) - support vector machine , pattern recognition (psychology) , block (permutation group theory) , artificial intelligence , convolutional neural network , computer science , feature extraction , classifier (uml) , land cover , contextual image classification , feature (linguistics) , remote sensing , land use , image (mathematics) , mathematics , geology , engineering , linguistics , philosophy , civil engineering , geometry
One of the materials essential for human life that must manage properly is the land. Land use and land cover (LULC) classification can help us how to manage land. The satellite can record images that can use as the data for LULC classification. This research aims to perform LULC classification using Convolutional Neural Network (CNN) on EuroSAT remote sensing image dataset taken from the Sentinel-2 satellite. CNN has become a well-known method to deal with image feature extraction. We used several CNN for feature extraction, such as VGG19, ResNet50, and InceptionV3. Then, we recalibrated the feature of CNN using Channel Squeeze & Spatial Excitation (sSE) block. We also used Support Vector Machine (SVM) and Twin SVM (TWSVM) as the classifier. VGG19 with sSE block and TWSVM achieved the highest experimental results with 94.57% accuracy, 94.40% precision, 94.40% recall, and 94.39% F1-score.