US 12,749,303 B2 · Automatic detection of sea floating objects from satellite imagery

Abstract

Methods and systems for macroalgae or marine debris detection are disclosed. A method includes accessing or receiving multispectral aerial images of a target region; preprocessing the aerial images; determining, using one or more characteristics of the aerial images, an image type for each of the aerial images; generating the one or more geospatial data images by: providing preprocessed aerial images of each image type to a machine learning algorithm trained using images having that image type; receiving, as outputs from each DCNN, image data indicating whether macroalgae are present in regions corresponding to each pixel of the aerial images; altering pixel values of the aerial images to visually indicate the presence of macroalgae in regions corresponding to the altered pixel values; and providing the one or more geospatial data images to the user device via a user interface. Other aspects, embodiments, and features are also claimed and described.

Claims20

Dependent claims

A method for remote object detection, comprising:

obtaining an image, the image comprising a plurality of spectral pixel values for each pixel of the image, the plurality of spectral pixel values corresponding to a plurality of wavelengths;
determining a spectral differencing value for each pixel of the image;
applying the plurality of spectral pixel values of the image and the spectral differencing value for each pixel of the image to a plurality of corresponding input channels of a trained deep learning model to obtain a probability value for each pixel of the image via an output channel of the trained deep learning model;
and
providing to a user object information of an object in the image based on the probability value for each pixel of the image.

2 more elements

A non-transitory computer-readable medium storing instructions executable by a processor to:

obtain an image, the image comprising a plurality of spectral pixel values for each pixel of the image, the plurality of spectral pixel values corresponding to a plurality of wavelengths;
determine a spectral differencing value for each pixel of the image;
apply the plurality of spectral pixel values of the image and the spectral differencing value for each pixel of the image to a plurality of corresponding input channels of a trained deep learning model to obtain a probability value for each pixel of the image via an output channel of the trained deep learning model;
and
provide to a user object information of an object in the image based on the probability value for each pixel of the image.

2 more elements

Dependencies are inferred from the claim text.

Automatic detection of sea floating objects from satellite imagery

Life of this patent

Automatic detection of sea floating objects from satellite imagery

Life of this patent

Abstract

Claims20