I have some doubts about this. Deep learning moves fast and DBNs are pretty much outdated models, even for unsupervised pre-training. It doesn't make much sense to me that unsupervised pre-training would help for this problem to begin with, seeing as their dataset totals around 65TB.

The paper is worth checking out: http://arxiv.org/abs/1509.03602 I haven't read it in full, but based on a quick skim, the convnet architectures they evaluated seem laughably tiny and shallow (at most three convolutional layers) by today's standards -- although I appreciate that there may be other constraints at play here (limits on training time etc.).

But to claim that DBNs are better suited for this problem than convnets based on these results is quite far-fetched. I'm confident that a convnet could crush these results, given enough effort and time spent on hyperparameter tuning.

I find this part particularly misleading (section 6, page 13): "shape/edge based features which are predominantly learned by various Deep architectures are not very useful in learning data representations for satellite imagery. This explains the fact why traditional Deep architectures are not able to converge to the global optima even for reasonably large as well as Deep architectures."

The whole point of learning features is so that they are better suited for the task at hand. If "shape/edge based features" are not suitable to perform a particular task, then a properly trained convnet should not learn them. I think the conclusions drawn from this work would have been very different if the chosen network architectures were more sensible.