Hyperspectral image analysis using machine learning

A hyperspectral imaging system comprising:

• a light source for emitting light;
• a hyperspectral imaging sensor;
• one or more computer processors;
• and
• a computer-readable medium storing computer program code configured, when executed by the one or more computer processors, to cause the one or more computer processors to perform a method comprising:
• controlling the light source to illuminate an object;
• receiving, from the hyperspectral imaging sensor, one or more hyperspectral images of the object captured in response to at least some of the emitted light being reflected by the object and being received at the hyperspectral imaging sensor, the one or more hyperspectral images comprising hyperspectral data;
• inputting the one or more hyperspectral images to a machine learning model trained using synthetic training samples generated from a plurality of predefined spectral signatures, comprising inputting to the trained machine learning model, pixel-by-pixel, each pixel of the one or more hyperspectral images, and the hyperspectral data associated with each pixel;
• using the trained machine learning model to spectrally un-mix, pixel-by-pixel, the hyperspectral data associated with each pixel so as to extract one or more spectral signatures from the hyperspectral data;
• and
• identifying, based on the one or more extracted spectral signatures, one or more materials comprised in the object.

A method of using hyperspectral imaging to identify one or more materials in an object, comprising:

• illuminating the object with light, wherein at least some of the light is reflected by the object;
• using a hyperspectral imaging sensor to capture, based on the reflected light, one or more hyperspectral images of the object, the one or more hyperspectral images comprising hyperspectral data;
• inputting the one or more hyperspectral images to a trained machine learning model, comprising inputting to the trained machine learning model, pixel-by-pixel, each pixel of the one or more hyperspectral images, and the hyperspectral data associated with each pixel;
• using the trained machine learning model to spectrally un-mix, pixel-by-pixel, the hyperspectral data associated with each pixel so as to extract one or more spectral signatures from the hyperspectral data;
• identifying, based on the one or more extracted spectral signatures, one or more materials comprised in the object;
• and
• based on the one or more identified materials comprised in the object, removing the object from a device transporting the object.