Compressive hyperspectral phasor imaging with single-pixel detection for spectral tasks

TL;DR

This paper introduces HyPIS, a novel compressive hyperspectral imaging method using single-pixel detection and phasor encoding, enabling real-time spectral classification with significantly reduced data requirements.

Contribution

The work presents a new spectral imaging technology that performs spectral tasks without high-resolution hyperspectral data, using optical encoding and single-pixel detection.

Findings

HyPIS reduces data requirements by two orders of magnitude.

It achieves real-time scene classification and recognition.

It performs accurately under low light and uneven lighting conditions.

Abstract

Spectral vision task plays a pivotal role in extracting discriminative spectral-spatial features from high-dimensional data, enabling fine-grained identification beyond human vision. Traditional methods usually involve first collecting rich spectral-spatial information and then using complex algorithms to digitally process it into scene classification and recognition. However, the complexity of processing massive three-dimensional (3D) hyperspectral datasets poses challenges for algorithms. Here, we demonstrate a compressive Hyperspectral Phasor Imaging with Single-pixel detection (HyPIS) that leverages highly compressed spatial-spectral data to achieve spectral task. Two optical encoders are used for wavelength-dependent sine- and cosine-encoding that transforms spectral signals into a two-dimensional (2D) phasor plot. By applying spatial-temporal illumination patterns, a single-pixel detector is enough to reconstruct the phasor image of the object. This allows to directly generate pixel-wise spectral task, bypassing 3D hyperspectral data. Our experiments show that HyPIS can perform real-time classification and recognition tasks of different scenes, reducing the required amount of data by two orders of magnitude, and it can still accurately classify under low light and uneven lighting conditions. This work develops a completely new spectral technology that enables spectral tasks to be performed without obtaining high-resolution hyperspectral datasets, holding promise for spectral applications in mobile devices, robotics, and satellite technologies.