Terrain estimation using low resolution imagery

Patent metadata

Field	Value
Publication number	US-2023386200-A1
Application number	US-202217804195-A
Country	US
Kind code	A1
Filing date	May 26, 2022
Priority date	May 26, 2022
Publication date	Nov 30, 2023
Grant date	—

Abstract

A computing system measures terrain coverage by: obtaining sample image data representing a multispectral image of a geographic region at a sample resolution; generating, based on the sample image data, an index array of pixels for a subject terrain in which each pixel has an index value that represents a predefined relationship between a first wavelength reflectance and a second wavelength reflectance; providing the index array to a trained calibration model to generate an estimated value based on the index array, the estimated value representing an estimated amount of terrain coverage within the geographic region for the subject terrain; and outputting the estimated value for the subject terrain. The trained calibration model may be trained based on training data representing one or more reference images of one or more training geographic regions containing the subject terrain at a higher resolution than the sample resolution.

First claim

1 . A method for measuring terrain coverage, the method comprising:
at a computing system:
obtaining sample image data representing a multispectral image of a geographic region at a sample resolution;
generating, based on the sample image data, an index array of pixels for a subject terrain in which each pixel has an index value that represents a predefined relationship between a first wavelength reflectance and a second wavelength reflectance;
providing the index array to a trained calibration model to generate an estimated value based on the index array, the estimated value representing an estimated amount of terrain coverage within the geographic region for the subject terrain;
wherein the trained calibration model is previously trained based on training data representing one or more reference images of one or more training geographic regions containing the subject terrain at a higher resolution than the sample resolution; and
outputting the estimated value for the subject terrain.

2 . The method of claim 1 , wherein the trained calibration model was trained by adjusting one or more weights of a function that represents a model-based mapping between the index values of the index array and the estimated value based on a target value generated from each of the one or more reference images.

3 . The method of claim 1 , further comprising, training an untrained calibration model to obtain the trained calibration model by:
obtaining the one or more training examples in which each training example includes at least:
a target value of the subject terrain within a training geographic region that is based on a reference image at the higher resolution, and
a training sample image of the subject terrain within the training geographic region at the sample resolution;
for each training example of the one or more training examples:
providing a training index array of the training sample image to the untrained calibration model to generate a training estimated value based on the training index array, the training estimated value representing an estimated amount of terrain coverage within the training geographic region for the subject terrain, and
adjusting one or more parameters of the untrained calibration model based on an error between the training estimated value and the target value over each of the one or more training examples to obtain the trained calibration model.

4 . The method of claim 3 , wherein each training example further includes a downsampled image of each reference image at the sample resolution; and
wherein the method further comprises:
downsampling the reference image or an index array of the reference image to obtain the downsampled image; and
at a regressor executed by the computing system or another computing system:
determining the error over the one or more training examples, and
adjusting the one or more parameters based on the error.

5 . The method of claim 1 , wherein the subject terrain includes vegetation canopy coverage; and
wherein the estimated value represents a fractional vegetation canopy coverage for the subject terrain within the geographic region.

6 . The method of claim 5 , wherein the first wavelength reflectance is near-infrared wavelength reflectance and the second wavelength reflectance is visible, red wavelength reflectance.

7 . A method performed by a computing system for training a calibration model for measuring terrain coverage of a geographic region, the method comprising:
obtaining a reference image of the geographic region at a reference resolution; determining, based on the reference image, a target value representing an amount of terrain coverage within the geographic region for a subject terrain; obtaining a sample image representing a multispectral image of the geographic region at a sample resolution that is lower than the reference resolution; generating, based on the sample image, an index array of pixels for the subject terrain in which each pixel has an index value that represents a predefined relationship between a first wavelength reflectance and a second wavelength reflectance; providing the index array to the calibration model to generate an estimated value representing an estimated amount of terrain coverage within the geographic region for the subject terrain; determining an error between the target value and the estimated value; and adjusting one or more parameters of the calibration model based on the error to obtain a trained calibration model.

8 . The method of claim 7 , further comprising:
generating, based on the reference image, an image mask that identifies, for each pixel, whether the subject terrain is present or not present at that pixel; wherein determining the target value is based on the image mask.

9 . The method of claim 8 , further comprising:
downsampling the reference image or the image mask to obtain a downsampled image having the sample resolution; and wherein adjusting the one or more parameters of the calibration model is further based on a reference mapping between pixel values of the downsampled image and the target value.

10 . The method of claim 7 , wherein adjusting the one or more parameters is performed by a regressor executed by the computing system.

11 . The method of claim 7 , wherein the subject terrain includes vegetation canopy coverage; and
wherein the estimated value represents a fractional vegetation canopy coverage for the subject terrain within the geographic region.

12 . The method of claim 11 , wherein the first wavelength reflectance is near-infrared wavelength reflectance and the second wavelength reflectance is visible, red wavelength reflectance.

13 . The method of claim 7 , wherein the target value and the index array form part of a training example; and wherein the method further comprising:
obtaining a plurality of training examples in which each training example includes a respective target value obtained from a respective reference image at the reference resolution and a respective index array at the sample resolution; and to obtain the trained calibration model, for each of the plurality of training examples:
determining a respective error between the target value and an estimated value generated by the calibration model from the index array of that training example, and
adjusting one or more parameters of the calibration model based on the respective error.

14 . The method of claim 7 , further comprising:
providing a copy of the trained calibration model to another computing system to generate a respective estimated value for the subject terrain at the trained calibration model using a sample image capturing a respective geographic region as input.

15 . A computing system component, comprising:
a data storage machine having instructions stored thereon executable by a logic machine to:
obtain sample image data representing a multispectral image of a geographic region at a sample resolution;
generate, based on the sample image data, an index array of pixels for a subject terrain in which each pixel has an index value that represents a predefined relationship between a first wavelength reflectance and a second wavelength reflectance;
provide the index array to a trained calibration model to generate an estimated value based on the index array, the estimated value representing an estimated amount of terrain coverage within the geographic region for the subject terrain;
wherein the trained calibration model is previously trained based on training data representing one or more reference images of one or more training geographic regions conta

Assignees

• Microsoft Technology Licensing Llc

Inventors

• De Moura Estevao Filho Roberto
• de Oliveira Nunes Leonardo
• Olsen Peder Andreas
• Badam Anirudh

Classifications

• G06V20/194 Primary

  using hyperspectral data, i.e. more or other wavelengths than RGB · CPC title

• G06V10/60

  relating to illumination properties, e.g. using a reflectance or lighting model · CPC title

• G06V10/774

  Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V20/188 Primary

  Vegetation · CPC title

• G06V10/82

  using neural networks · CPC title