Multi-threshold splitting tree algorithm to reduce the number of filters in programmable hyperspectral imaging for fast multi-target classification

Abstract

Programmable hyperspectral imaging is a promising and efficient technique for fast target classification by coding hyperspectral post-processing algorithms as spectral transmittances, which enables such post-processing to be directly performed by special optical dispersive element during the process of optical imaging. Compared with conventional hyperspectral imaging and post-processing techniques, it shows significant advantages of fast image acquisition, post-processing free, and a much lower load of data transmission and storage. However, when multi-target classification tasks are encountered, the speed would decrease seriously due to the requirement of a large number of filters. In this study, a novel splitting strategy is proposed to reduce the number of filters in programmable hyperspectral imaging for fast multi-target classification while maintaining the classification performance. Numerical simulation experiments were performed on six publicly available hyperspectral data sets. Compared with the conventional splitting strategies, the proposed splitting strategy can reduce the number of filters by 25% to 80% and achieve similar classification performance, which is of great significance to improve the speed of multi-target classification with programmable hyperspectral imaging technique.