US 12,260,596 B2
Hyperspectral image compression using a feature extraction model
Richard Pattison, Goleta, CA (US)
Assigned to Apeel Technology, Inc., Goleta, CA (US)
Filed by Apeel Technology, Inc., Goleta, CA (US)
Filed on Feb. 8, 2022, as Appl. No. 17/667,151.
Claims priority of provisional application 63/153,430, filed on Feb. 25, 2021.
Prior Publication US 2022/0270298 A1, Aug. 25, 2022
Int. Cl. G06T 9/00 (2006.01); G06V 10/58 (2022.01)
CPC G06T 9/00 (2013.01) [G06V 10/58 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for reducing an amount of memory required to store hyperspectral images of an object, the method comprising:
obtaining, by one or more computers, tensor data representing a hyperspectral image, wherein the hyperspectral image includes a first portion that depicts an object and a second portion that depicts at least a portion of a surrounding environment where the object is located, wherein the object is a first type, and wherein the hyperspectral image captures data within a wavelength range, and wherein the data within the wavelength range is usable for inferencing conditions of objects of the first type;
identifying, by the one or more computers, a portion of the tensor data representing the hyperspectral image that corresponds to the first portion of the hyperspectral image;
providing, by the one or more computers, the identified portion of the tensor data representing the hyperspectral image as an input to a feature extraction model, wherein the feature extraction model was trained using a training dataset to process portions of tensor data of other hyperspectral images to determine one or more matrix structures associated with the other hyperspectral images;
obtaining, by the one or more computers, the one or more matrix structures as output from the feature extraction model, wherein the one or more matrix structures represent a subset of features extracted from the identified portion of the tensor data representing the hyperspectral image, and wherein the subset of the features extracted from the identified portion comprises distinguishing features usable to inference a current condition of the object; and
storing, by the one or more computers, the one or more matrix structures in a memory device.