System for simplified generation of systems for broad area geospatial object detection

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.

What is claimed is: 
     
         1 . A system for broad area geospatial object detection comprising:
 at least one computing device comprising a processor, a memory, a network interface, and a plurality of programming instructions stored in the memory and operable on the processor;   a machine learning classifier training and verification module comprising programming instructions operating on the processor of one of the computing devices to cause the respective processor to:
 train a plurality of machine learning classifier elements, each running a machine learning protocol parameterized with an object classification model trained to recognize an object of interest, using a plurality of labeled and unlabeled orthorectified geospatial training images; and 
   a model-based object classifier comprising programming instructions operating on the processor of one of the computing devices to cause the respective processor to:
 using the plurality of trained machine learning classifier elements, 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. 
   
     
     
         2 . The system of  claim 1 , further comprising:
 an object model creation module comprising programming instructions operating on the processor of one of the computing devices to cause the respective 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 
 train an object classification model to classify only the object of interest. 
   
     
     
         3 . The system of  claim 2 , wherein the object model creation module, 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. 
     
     
         4 . A method for broad area geospatial object detection, the method comprising the steps of:
 (a) train a plurality of machine learning classifier elements, each running a machine learning protocol parameterized with an object classification model trained to recognize an object of interest, using a plurality of labeled and unlabeled orthorectified geospatial training images;   (b) using the plurality of trained machine learning classifier elements, analyze a plurality of resolution scale-corrected, unanalyzed orthorectified geospatial image segments for presence of at least one object of interest; and   (c) report the presence and location of any objects of interest found.   
     
     
         5 . The method of  claim 4 , further comprising the steps of:
 using an object model creation module comprising programming instructions operating on the processor of a computing device:
 (d) receiving a plurality of orthorectified geospatial images in which an object of interest has been identified; 
 (e) retrieving a plurality of orthorectified geospatial images wherein objects that are not the object of interest have been identified; and 
 (f) training an object classification model to classify only the object of interest. 
   
     
     
         6 . The method of  claim 5 , wherein the object model creation module, 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.