Abstract: A method of processing hyperspectral data includes receiving the hyperspectral data. The hyperspectral data includes spectral data for each pixel in a two-dimensional array of pixels, and for each spectral band in a set of multiple spectral bands associated with each pixel. The hyperspectral data is converted into one-dimensional spectra. Each one-dimensional spectrum includes, for a single pixel of the pixels, the spectral data for each spectral band in the set of multiple spectral bands associated with the single pixel. Each one-dimensional spectrum is inputted to a trained transformer neural network. For each one-dimensional spectrum, the trained transformer neural network is used to spectrally un-mix the spectral data in the set of multiple spectral bands.

Abstract: Hyperspectral imaging is used to identify one or more materials in an object. The object is illuminated with light. At least some of the light is reflected by the object. A hyperspectral imaging sensor captures, based on the reflected light, one or more hyperspectral images of the object, the one or more hyperspectral images include hyperspectral data. The one or more hyperspectral images are input to a trained machine learning model. The trained machine learning model spectrally un-mixes the hyperspectral data so as to extract one or more spectral signatures from the hyperspectral data. Based on the one or more extracted spectral signatures, one or more materials comprised in the object are extracted. Another trained machine learning model is used to detect the shape of the object.

Abstract: A process for reducing time of transmission for single-band, multiple-band or hyperspectral imagery using Machine Learning based compression is disclosed. The process uses Machine Learning to compress single-band, multiple-band and hyperspectral imagery, thereby decreasing the needed bandwidth and storage-capacity requirements for efficient transmission and data storage. The reduced file size for transmission accelerate the communications and reduces the transmission time. This enhances communications systems where there is a greater need for on or near real-time transmission, such as mission critical applications in national security, aerospace and natural resources.

Abstract: A process for reducing time of transmission for single-band, multiple-band or hyperspectral imagery using Machine Learning based compression is disclosed. The process uses Machine Learning to compress single-band, multiple-band and hyperspectral imagery, thereby decreasing the needed bandwidth and storage-capacity requirements for efficient transmission and data storage. The reduced file size for transmission accelerate the communications and reduces the transmission time. This enhances communications systems where there is a greater need for on or near real-time transmission, such as mission critical applications in national security, aerospace and natural resources.