TY - JOUR
T1 - Land use and land cover classification using CNN, SVM, and Channel Squeeze & Spatial Excitation block
AU - Dewangkoro, H. I.
AU - Arymurthy, Aniati Murni
N1 - Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
PY - 2021/4/7
Y1 - 2021/4/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85104193194&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/704/1/012048
DO - 10.1088/1755-1315/704/1/012048
M3 - Conference article
AN - SCOPUS:85104193194
VL - 704
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
SN - 1755-1307
IS - 1
M1 - 012048
T2 - International Symposium of Geoscience, Oil and Gas Engineering, Sustainable and Environmental Technology 2020, GEOSOSTEK 2020
Y2 - 19 December 2020 through 20 December 2020
ER -
