# US 10,372,985 B2 · System for simplified generation of systems for broad area geospatial object detection

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- Number: US 10,372,985 B2
- Document ID: US10372985B2
- Status: Granted
- Kind: B2
- Application number: 15/906,348
- Filed: 2018-02-27
- Published: 2018-07-05
- Granted: 2019-08-06
- Earliest claimed: 2015-08-26
- Assignees: DigitalGlobe, Inc.
- Applicants: DigitalGlobe, Inc.
- Inventors: Adam Estrada; Kevin Green; Andrew Jenkins
- Web page: https://patentgrep.com/patent/US10372985B2

## CPC

- G06N 3/08: Learning methods
- G06V 10/50: by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2155: {characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling}
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06F 18/24: Classification techniques
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/09: Supervised learning
- G06T 7/73: using feature-based methods
- G06T 15/50: Lighting effects
- G06T 17/05: Geographic models
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06V 10/7753: {Incorporation of unlabelled data, e.g. multiple instance learning [MIL]}
- G06V 10/776: Validation; Performance evaluation
- G06V 20/13: Satellite images
- G06V 20/176: {Urban or other man-made structures}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/20: Ensemble learning
- G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215)
- G06T 2207/10032: Satellite or aerial image; Remote sensing
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30181: Earth observation
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

## Claims

### Claim 1

A system for broad area geospatial object detection comprising:
an object model creation module comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the processor to:
receive a plurality of orthorectified geospatial images in which an object of interest has been identified;
retrieve a plurality of orthorectified geospatial images wherein objects that are not the object of interest, have been identified; and
create an object classification model trained to classify only the object of interest;
a machine learning classifier training and verification computer comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the processor to:
accept at least one object classification model;
retrieve a plurality of labeled and unlabeled orthorectified geospatial training images each comprising the object of interest;
train a plurality of pre-built machine learning classifier elements, each running a pre-programmed machine learning protocol parameterized with the classification model, using the plurality of labeled and unlabeled orthorectified geospatial training images each comprising the object of interest; and
for each trained machine learning classifier element, verify performance in classifying the object of interest using a plurality of unlabeled orthorectified geospatial training images comprising the object of interest and a plurality of unlabeled orthorectified geospatial training images that do not contain the object of interest; and
a model-based object classifier comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the processor to:
retrieve the plurality of trained machine learning elements for the object of interest;
analyze a plurality of resolution scale-corrected, unanalyzed orthorectified geospatial image segments for presence of at least one object of interest; and
report the presence and location of any objects of interest found.

### Claim 2

A method for broad area geospatial object detection, the method comprising the steps of:
(a) retrieving a plurality of geospatial training images comprising an object of interest that is labeled, and a second plurality of geospatial training images that do not contain the object of interest;
(b) isolating a set of visual features unique to the object of interest using an object model creation module;
(c) employing the set of visual features unique to the object of interest to parameterize at least one machine learning classifier element running at least one pre-programmed machine learning protocol using a machine learning classifier element training and verification module;
(d) training the machine learning classifier elements to identify the object of interest using a plurality of training geospatial images with the object of interest labeled in one subset and not labeled in a second subset within the machine learning classifier element training and verification module;
(e) refining the trained machine learning classifier elements using geospatial training images not containing the object of interest, some of which comprise other irrelevant objects; and
(e) analyzing previously unanalyzed, scale-corrected geospatial images for presence of the object of interest using the trained machine learning classified element; and
(f) reporting the presence and location of any objects of interest found.