Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection

For applications based on hyperspectral imagery (HSI), selecting informative and representative bands without the degradation of performance is a challenging task in the context of big data. In this paper, an unsupervised band selection method, scalable one-pass self-representation learning (SOP-SRL), is proposed to address this problem by processing data in a streaming fashion without storing the entire data. SOP-SRL embeds band selection into a scalable self-representation learning, which is formulated as an adaptive linear combination of regression-based loss functions, with the row-sparsity constraint. To further enhance the representativeness of bands, the local similarity between samples constructed by the selected bands is dynamically measured by means of graph-based regularization term in the embedded space. Moreover, a cache with memory function that reflects the quality of bands in the historical data is designed to keep the consistency between data coming at different times and guide subsequent band selection. An efficient algorithm is developed to optimize the SOP-SRL model. The HSI classification is conducted on three public data sets, and the experimental results validate the superiority of SOP-SRL in terms of performance and time when compared with other state-of-the-art band selection methods.