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In this paper, we investigate the impact of segmentation algorithms as apreprocessing step for classification of remote sensing images in a deeplearning framework. Especially, we address the issue of segmenting the imageinto regions to be classified using pre-trained deep neural networks as featureextractors for an SVM-based classifier. An efficient segmentation as apreprocessing step helps learning by adding a spatially-coherent structure tothe data. Therefore, we compare algorithms producing superpixels with moretraditional remote sensing segmentation algorithms and measure the variation interms of classification accuracy. We establish that superpixel algorithms allowfor a better classification accuracy as a homogenous and compact segmentationfavors better generalization of the training samples.

How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?