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The band selection in the hyperspectral image (HSI) data processing is animportant task considering its effect on the computational complexity andaccuracy. In this work, we propose a novel framework for the band selectionproblem: Self-Representation Learning (SRL) with Sparse 1D-OperationalAutoencoder (SOA). The proposed SLR-SOA approach introduces a novel autoencodermodel, SOA, that is designed to learn a representation domain where the dataare sparsely represented. Moreover, the network composes of 1D-operationallayers with the non-linear neuron model. Hence, the learning capability ofneurons (filters) is greatly improved with shallow architectures. Using compactarchitectures is especially crucial in autoencoders as they tend to overfiteasily because of their identity mapping objective. Overall, we show that theproposed SRL-SOA band selection approach outperforms the competing methods overtwo HSI data including Indian Pines and Salinas-A considering the achieved landcover classification accuracies. The software implementation of the SRL-SOAapproach is shared publicly at https://github.com/meteahishali/SRL-SOA.

SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection