MATLAB PROJECT
Automatic Land Cover
Reconstruction From Historical Aerial Images: An Evaluation of Features
Extraction and Classification Algorithms
Abstract:
The land cover reconstruction from monochromatic historical aerial
images is a challenging task that has recently attracted an increasing interest
from the scientific community with the proliferation of large-scale
epidemiological studies involving retrospective analysis of spatial patterns.
However, the efforts made by the computer vision community in remote-sensing
applications are mostly focused on prospective approaches through the analysis
of high-resolution multi-spectral data acquired by the advanced spatial
programs. Hence, four contributions are proposed in this paper. They aim at
providing a comparison basis for the future development of computer vision
algorithms applied to the automation of the land cover reconstruction from
monochromatic historical aerial images. First, a new multi-scale multi-date
dataset composed of 4.9 million non-overlapping annotated patches of the France
territory between 1970 and 1990 has been created with the help of geography
experts. This dataset has been named HistAerial. Second, an extensive
comparison study of the state-of-the-art texture features extraction and
classification algorithms, including deep convolutional neural networks
(DCNNs), has been performed. It is presented in the form of an evaluation. Third,
a novel low-dimensional local texture filter named rotated-corner local binary
pattern (R-CRLBP) is presented as a simplification of the binary gradient
contours filter through the use of an orthogonal combination representation.
Finally, a novel combination of low-dimensional texture descriptors, including
the R-CRLBP filter, is introduced as a light combination of local binary
patterns (LCoLBPs). The LCoLBP filter achieved state-of-the-art results on the
HistAerial dataset while conserving a relatively low-dimensional feature vector
space compared with the DCNN approaches (17 times shorter).
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