Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks
This addresses band selection for hyperspectral image analysis, which is incremental as it builds on existing attention and CNN methods.
The paper tackled the problem of selecting informative bands from hyperspectral images for classification, resulting in high-quality classification and the creation of compact band sets that retain meaningful features.
This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images. The proposed approach re-uses convolutional activations at different depths, identifying the most informative regions of the spectrum with the help of gating mechanisms. Our attention techniques are modular and easy to implement, and they can be seamlessly trained end-to-end using gradient descent. Our rigorous experiments showed that deep models equipped with the attention mechanism deliver high-quality classification, and repeatedly identify significant bands in the training data, permitting the creation of refined and extremely compact sets that retain the most meaningful features.