Patent Document ID: 20170364733
Application ID: 15608894
Patent Status: 0

Claim One:
1. A system for simplified generation of systems 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: receives a plurality labeled positive training orthorectified geospatial images in at least some of which an object of interest has been identified; retrieves a plurality of labeled negative training orthorectified geospatial images where objects that are not the object of interest, at least one of which closely resembles the object of interest, have been identified; programmatically isolates features found in the objects of interest but not in the irrelevant training objects; and creates at least one object of interest classification model using the features unique to 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: accepts at least one classification model; retrieves a plurality of labeled and unlabeled orthorectified geospatial training images each comprising the object of interest; trains 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, verifies 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: retrieve the plurality of trained machine learning elements for the object of interest; analyzes a plurality of resolution scale-corrected, unanalyzed orthorectified geospatial image segments for presence of at least one object of interest; and reports the presence and location of any objects of interest found.