Title: Efficient collection of training data for sub-pixel land cover classification using neural networks
Authors: Heremans, Stien ×
Bossyns, Bert
Eerens, Herman
Van Orshoven, Jos #
Issue Date: Aug-2011
Publisher: International Institute for Aerial Survey and Earth Sciences
Series Title: International Journal of Applied Earth Observation and Geoinformation vol:13 issue:4 pages:657-667
Abstract: Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications
of satellite images. They have successfully been applied for estimating crop areas through sub-pixel
classification of medium to low resolution images. Before a network can be used for classification and
estimation, however, it has to be trained. The collection of the reference area fractions needed to train
an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the
efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting
area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing
trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because
of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOTVGT
and MODIS were used as the network inputs. The results showed that accurate regional crop area
estimation (R2 > 80%) is possible using only 1% of the entire area for network training, provided that the
training samples used are representative for the land use variability present in the study area. Limiting the
training samples to a specific subset of the population, either geographically or thematically, significantly
decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data
from one season to estimate area fractions in another season is not to be recommended. The interannual
variability observed in the endmembers’ spectral signatures underlines the importance of using up-todate
training samples. It can thus be concluded that the representativeness of the training samples, both
regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.
ISSN: 0303-2434
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division Forest, Nature and Landscape Research
× corresponding author
# (joint) last author

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