Turkish Journal of Electrical Engineering and Computer Sciences




Inspired by the surprising performances of deep generative models, in this paper we present the preliminary results of an overly ambitious task: estimating computationally the additional spectral bands of a color aerial image. We have harnessed the expressive power of deep generative models to estimate the distribution of mostly infrared bands of aerial scenes, using only color RGB channels as input. Our approach has been tested from multiple aspects, including the reconstruction error of the additional bands and the effect of estimated bands on scene classification performance, as well as through the transfer potential of the trained network to a distinct dataset. To our surprise, the initial experiments have shown us that deep generative models can indeed learn to estimate additional bands up to a certain degree and can thus computationally reinforce datasets stemming from color-only sensors.


Aerial scene classification, auto-encoder, generative models, convolutional neural network, spectral super-resolution

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