TY - GEN
T1 - COMPARING CNN ARCHITECTURES FOR LAND COVER CLASSIFICATION ON MULTISPECTRAL IMAGES
AU - Engelbrecht, Bryce
AU - van Zyl, Terence L.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - We investigate the benefits of using multispectral images for land cover classification. To perform this comparative analysis, we present a novel model, LandNet which is configurable with multiple deep residual networks to extract features on several combinations of bands. We consider both the classification accuracy of the various LandNet configurations. We perform this study on the EuroSAT and BigEarthNet datasets, both of which contain multispectral images from the Sentinel-2 mission. On EuroSAT, we convincingly demonstrate marked improvements in the accuracy of around 1% to 97.815% when using additional bands compared to merely using the RGB bands. On BigEarthNet we show the additional bands are able to improve the recall of the LandNet by 0.04. We achieve a precision score of 0.85, recall of 0.80 and an F-score of 0.82. The precision, recall and F-score we achieve outperform prior results achieved on BigEarthNet.
AB - We investigate the benefits of using multispectral images for land cover classification. To perform this comparative analysis, we present a novel model, LandNet which is configurable with multiple deep residual networks to extract features on several combinations of bands. We consider both the classification accuracy of the various LandNet configurations. We perform this study on the EuroSAT and BigEarthNet datasets, both of which contain multispectral images from the Sentinel-2 mission. On EuroSAT, we convincingly demonstrate marked improvements in the accuracy of around 1% to 97.815% when using additional bands compared to merely using the RGB bands. On BigEarthNet we show the additional bands are able to improve the recall of the LandNet by 0.04. We achieve a precision score of 0.85, recall of 0.80 and an F-score of 0.82. The precision, recall and F-score we achieve outperform prior results achieved on BigEarthNet.
UR - http://www.scopus.com/inward/record.url?scp=85126015572&partnerID=8YFLogxK
U2 - 10.1109/IGARSS47720.2021.9553337
DO - 10.1109/IGARSS47720.2021.9553337
M3 - Conference contribution
AN - SCOPUS:85126015572
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5378
EP - 5381
BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
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 -