TY - GEN
T1 - LULC image classification with convolutional neural network
AU - Balarabe, Anas Tukur
AU - Jordanov, Ivan
N1 - Funding Information:
ACKNOWLEDGEMENT: this work is part of a PhD research sponsored by Petroleum Technology Development Fund (PTDF)-Nigeria
Publisher Copyright:
©2021 IEEE.
PY - 2021/10/12
Y1 - 2021/10/12
N2 - The topic of land use and land cover classification (LULC) has attracted the interest of many researchers in recent times. A variety of techniques have been proposed for LULC and while some of them are semantic segmentation-based, others are classifying an entire image to determine its class. The semantic segmentation approaches label objects as members of a class by assigning a different colour to each class. In this work, we investigate class heterogeneity, which so far, to the best of our knowledge, has not been explored in LULC or scene classification. We carefully cluster the 21 classes of the UC Merced dataset into four superclasses based on their textural, spectral, or structural similarities and use the dataset to test the performance of our model. We also demonstrate the efficiency and accuracy of our deep learning approach, reporting a superior performance of our model in terms of Accuracy, Precision, Recall, and F1 score.
AB - The topic of land use and land cover classification (LULC) has attracted the interest of many researchers in recent times. A variety of techniques have been proposed for LULC and while some of them are semantic segmentation-based, others are classifying an entire image to determine its class. The semantic segmentation approaches label objects as members of a class by assigning a different colour to each class. In this work, we investigate class heterogeneity, which so far, to the best of our knowledge, has not been explored in LULC or scene classification. We carefully cluster the 21 classes of the UC Merced dataset into four superclasses based on their textural, spectral, or structural similarities and use the dataset to test the performance of our model. We also demonstrate the efficiency and accuracy of our deep learning approach, reporting a superior performance of our model in terms of Accuracy, Precision, Recall, and F1 score.
KW - convolutional neural networks (CNN)
KW - deep learning
KW - LULC
KW - multilabel
KW - scene classification
UR - http://www.scopus.com/inward/record.url?scp=85128293826&partnerID=8YFLogxK
U2 - 10.1109/IGARSS47720.2021.9555015
DO - 10.1109/IGARSS47720.2021.9555015
M3 - Conference contribution
AN - SCOPUS:85128293826
SN - 9781665447621
T3 - IEEE International Geoscience and Remote Sensing Symposium proceedings
SP - 5985
EP - 5988
BT - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -
