Advanced image enhancements for accurate processing, exploitation, and dissemination
A system and method for object monitoring, detection, and segmentation in electro-optical (EO) satellite imagery data comprising an image processing engine configured to prepare EO data for analysis by an inference engine which utilizes one or more trained models to perform object detection or image segmentation. The workflow begins with ingesting satellite data, followed by de-hazing to remove atmospheric interference. Image enhancement improves resolution and geo-registration ensures precise spatial alignment. The processed image is then fed into a machine learning-based object detection or image segmentation network, trained to identify specific objects of interest. This integrated approach leverages advanced technologies to extract actionable insights from satellite data, enabling efficient and precise object monitoring, detection, and segmentation.
1. An object monitoring, detection, and segmentation platform, comprising:
a computing device comprising a memory and a processor;
a machine learning algorithm configured to perform object detection or image segmentation on an input in order to generate as output an enhanced image;
an image processing engine comprising a first plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
receive satellite imagery data;
produce analysis ready data (ARD) by:
performing image dehazing on the satellite imagery data;
performing resolution enhancement on the satellite imagery data; and
performing geospatial alignment on the satellite imagery data; and
an inference engine comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
use the ARD as an input to the machine learning algorithm to produce an enhanced image associated with the satellite imagery data.
2. The platform of claim 1 , wherein the satellite imagery data comprises mid-resolution images.
3. The platform of claim 1 , wherein the satellite imagery is electro-optical imagery.
4. The platform of claim 1 , wherein the image dehazing is performed using a dehazing model.
5. The platform of claim 4 , wherein the dehazing model is developed using a generative adversarial network.
6. The platform of claim 1 , wherein the resolution enhancement is performed using a super-resolution technique.
7. The platform of claim 1 , wherein the machine learning algorithm is a computer vision algorithm.
8. The platform of claim 1 , wherein the machine learning algorithm is a convolutional neural network.
9. The platform of claim 1 , wherein the enhanced image comprises one or more bounding boxes and one or more class labels.
10. The platform of claim 1 , wherein the satellite imagery data comprises high-resolution images.
11. A method for object monitoring, detection, and segmentation, comprising the steps of:
training a machine learning algorithm configured to perform object detection or image segmentation on an input in order to generate as output an enhanced image;
receiving satellite imagery data;
producing analysis ready data (ARD) by:
performing image dehazing on the satellite imagery data;
performing resolution enhancement on the satellite imagery data; and
performing geospatial alignment on the satellite imagery data; and
using the ARD as an input to the machine learning algorithm to produce an enhanced image associated with the satellite imagery data.
12. The method of claim 11 , wherein the satellite imagery data comprises mid-resolution images.
13. The method of claim 11 , wherein the satellite imagery is electro-optical imagery.
14. The platform of claim 11 , wherein the image dehazing is performed using a dehazing model.
15. The method of claim 14 , wherein the dehazing model is developed using a generative adversarial network.
16. The method of claim 11 , wherein the resolution enhancement is performed using a super-resolution technique.
17. The method of claim 11 , wherein the machine learning algorithm is a computer vision algorithm.
18. The method of claim 11 , wherein the machine learning algorithm is a convolutional neural network.
19. The method of claim 11 , wherein the enhanced image comprises one or more bounding boxes and one or more class labels.
20. The method of claim 11 , wherein the satellite imagery data comprises high-resolution images.