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This paper introduces new attention-based convolutional neural networks forselecting bands from hyperspectral images. The proposed approach re-usesconvolutional activations at different depths, identifying the most informativeregions of the spectrum with the help of gating mechanisms. Our attentiontechniques are modular and easy to implement, and they can be seamlesslytrained end-to-end using gradient descent. Our rigorous experiments showed thatdeep models equipped with the attention mechanism deliver high-qualityclassification, and repeatedly identify significant bands in the training data,permitting the creation of refined and extremely compact sets that retain themost meaningful features.

Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks