  Deep learning-based informative band selection methods on hyperspectral
images (HSI) recently have gained intense attention to eliminate spectral
correlation and redundancies. However, the existing deep learning-based methods
either need additional post-processing strategies to select the descriptive
bands or optimize the model indirectly, due to the parameterization inability
of discrete variables for the selection procedure. To overcome these
limitations, this work proposes a novel end-to-end network for informative band
selection. The proposed network is inspired by the advances in concrete
autoencoder (CAE) and dropout feature ranking strategy. Different from the
traditional deep learning-based methods, the proposed network is trained
directly given the required band subset eliminating the need for further
post-processing. Experimental results on four HSI scenes show that the proposed
dropout CAE achieves substantial and effective performance levels outperforming
the competing methods.
