  In this paper, we investigate the impact of segmentation algorithms as a
preprocessing step for classification of remote sensing images in a deep
learning framework. Especially, we address the issue of segmenting the image
into regions to be classified using pre-trained deep neural networks as feature
extractors for an SVM-based classifier. An efficient segmentation as a
preprocessing step helps learning by adding a spatially-coherent structure to
the data. Therefore, we compare algorithms producing superpixels with more
traditional remote sensing segmentation algorithms and measure the variation in
terms of classification accuracy. We establish that superpixel algorithms allow
for a better classification accuracy as a homogenous and compact segmentation
favors better generalization of the training samples.
