FN Archimer Export Format
PT J
TI Assessment of Deep Learning Techniques for Land Use Land Cover Classification in Southern New Caledonia
BT 
AF Rousset, Guillaume
   Despinoy, Marc
   Schindler, Konrad
   Mangeas, Morgan
AS 1:1,2;2:1;3:3;4:4;
FF 1:;2:;3:;4:;
C1 ESPACE-DEV, Univ New Caledonia, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ Réunion, 98800 New Caledonia, France
   ISEA, Univ New Caledonia, 98800 New Caledonia, France
   Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
   Institut de Recherche pour le Développement, UMR 9220 ENTROPIE (Institut de Recherche pour le Développement, Université de la Réunion, IFREMER, Université de la Nouvelle-Calédonie, Centre National de la Recherche Scientifique), 98800 New Caledonia, France
C2 UNIV NOUVELLE CALEDONIE, FRANCE
   UNIV NOUVELLE CALEDONIE, FRANCE
   ETH ZURICH, SWITZERLAND
   IRD, FRANCE
UM ENTROPIE
IN WOS Cotutelle UMR
   DOAJ
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 5.349
TC 26
UR https://archimer.ifremer.fr/doc/00698/81051/85105.pdf
   https://archimer.ifremer.fr/doc/00698/81051/85106.zip
LA English
DT Article
DE ;New Caledonia;remote sensing;land use;land cover;deep learning;XGBoost;neural network;neo-channels
AB Land use (LU) and land cover (LC) are two complementary pieces of cartographic information used for urban planning and environmental monitoring. In the context of New Caledonia, a biodiversity hotspot, the availability of up-to-date LULC maps is essential to monitor the impact of extreme events such as cyclones and human activities on the environment. With the democratization of satellite data and the development of high-performance deep learning techniques, it is possible to create these data automatically. This work aims at determining the best current deep learning configuration (pixel-wise vs. semantic labelling architectures, data augmentation, image prepossessing, …), to perform LULC mapping in a complex, subtropical environment. For this purpose, a specific data set based on SPOT6 satellite data was created and made available for the scientific community as an LULC benchmark in a tropical, complex environment using five representative areas of New Caledonia labelled by a human operator: four used as training sets, and the fifth as a test set. Several architectures were trained and the resulting classification was compared with a state-of-the-art machine learning technique: XGboost. We also assessed the relevance of popular neo-channels derived from the raw observations in the context of deep learning. The deep learning approach showed comparable results to XGboost for LC detection and over-performed it on the LU detection task (61.45% vs. 51.56% of overall accuracy). Finally, adding LC classification output of the dedicated deep learning architecture to the raw channels input significantly improved the overall accuracy of the deep learning LU classification task (63.61% of overall accuracy). All the data used in this study are available on line for the remote sensing community and for assessing other LULC detection techniques. 
PY 2021
PD JUL
SO Remote Sensing
SN 2072-4292
PU MDPI AG
VL 13
IS 12
UT 000666374300001
DI 10.3390/rs13122257
ID 81051
ER

EF