Systems and methods for generating synthetic satellite image training data for machine learning models

A system for generating synthetic training images configured to train a machine learning classifier to detect a presence of one or more objects in an image, the system comprising:

• a memory;
• and
• one or more processors;
• wherein the memory stores one or more programs that when executed by the one or more processors, cause the one or more processors to:
• receive a plurality of object images, wherein each object image of the plurality of object images comprises an object to be identified by the machine learning classifier;
• segment the image of the object from each object image of the plurality of object images to generate a plurality of cutout object images;
• receive a plurality of background images;
• normalize a first cutout object image of the plurality of cutout object images and a first background image of the plurality of background images;
• determine a placement area of the first background image to place the first cutout object image upon at least in part by identifying a region of the background image that is suitable for realistic placement of the cutout object image;
• superimpose the first cutout object image onto the determined placement area of the first background image to generate a first synthetic training image;
• and
• annotate the generated first synthetic training image with a location of the superimposed first cutout object image on the generated synthetic training image.

A non-transitory computer readable storage medium storing one or more programs for generating synthetic training images configured to train a machine learning classifier to detect a presence of one or more objects in an image, the programs for execution by one or more processors of an electronic device that when executed by the device, cause the device to:

• receive a plurality of object images, wherein each object image of the plurality of object images comprises an object to be identified by the machine learning classifier;
• segment the image of the object from each object image of the plurality of object images to generate a plurality of cutout object images;
• receive a plurality of background images;
• normalizing a first cutout object image of the plurality of cutout object images and a first background image of the plurality of background images;
• determine a placement area of the first background image to place the first cutout object image upon at least in part by identifying a region of the background image that is suitable for realistic placement of the cutout object image;
• superimpose the first cutout object image onto the determined placement area of the first background image to generate a first synthetic training image;
• and
• annotate the generated first synthetic training image with a location of the superimposed first cutout object image on the generated synthetic training image.