Laguerre-Gauss Preprocessing: Line Profiles as Image Features for Aerial Images Classification
This work addresses feature reduction for aerial image classification, offering an incremental improvement in efficiency for remote sensing applications.
The authors tackled the problem of high-dimensional feature spaces in aerial image classification by proposing a Laguerre-Gauss preprocessing method that reduces feature space size while preserving classification information, achieving similar performance with simpler models compared to complex ones on a challenging dataset.
An image preprocessing methodology based on Fourier analysis together with the Laguerre-Gauss Spatial Filter is proposed. This is an alternative to obtain features from aerial images that reduces the feature space significantly, preserving enough information for classification tasks. Experiments on a challenging data set of aerial images show that it is possible to learn a robust classifier from this transformed and smaller feature space using simple models, with similar performance to the complete feature space and more complex models.