  The band selection in the hyperspectral image (HSI) data processing is an
important task considering its effect on the computational complexity and
accuracy. In this work, we propose a novel framework for the band selection
problem: Self-Representation Learning (SRL) with Sparse 1D-Operational
Autoencoder (SOA). The proposed SLR-SOA approach introduces a novel autoencoder
model, SOA, that is designed to learn a representation domain where the data
are sparsely represented. Moreover, the network composes of 1D-operational
layers with the non-linear neuron model. Hence, the learning capability of
neurons (filters) is greatly improved with shallow architectures. Using compact
architectures is especially crucial in autoencoders as they tend to overfit
easily because of their identity mapping objective. Overall, we show that the
proposed SRL-SOA band selection approach outperforms the competing methods over
two HSI data including Indian Pines and Salinas-A considering the achieved land
cover classification accuracies. The software implementation of the SRL-SOA
approach is shared publicly at https://github.com/meteahishali/SRL-SOA.
