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

Abstract

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 1 .

Task-oriented autonomous representation of visual inputs to facilitate robot goal achievement

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State Representation Learning (SRL) is a field in Robotics and Artificial Intelligence that studies how to encode the observations of an environment in a way that facilitates performing specific tasks. A common approach is using autoencoders and learning to reproduce the same state from a low-dimensional representation [1,2,3]. Although very task-independent, this method learns to encode features that may not be relevant to the task in which the encoding will be used.

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