I'm currently a little stuck with a problem, that sounds easier than it is (at least for me):
Let's say you have satellite images taken from LEO that show an approximately 1000 km wide area (the image axis of the camera is more or less perpendicular to the ground). There is no additional location data stored in the image, so no way of directly extracting the position the image was taken).
What I want to do is write a program (in python on a raspberry pi with coral usb) that can find the location the image was taken from by matching it to a map of earth. this should be done automatically (more or less in real time) for the purpose of calculating the orbit of the satellite taking the images.
I've no problem calculating the orbit, once I have location data (even if it's very noisy), using a technique based on an Extended Kalman Filter.
Matching a satellite image to a map of earth by just using the image data, on the other hand.... I honestly don't even know where to start.
I know this is an incredibly unspecific question and not related to a specific problem, but maybe someone could point me in the right direction...
Just to give you an idea how those unprocessed images from LEO look, I included a few reasonably good images taken over one orbit of earth.
I took those images with a NIR camera through a small port hole on the International Space Station last year. Resolution of the images I included have been only 640x480 (by mistake!), but image resolution should be around 4k.
These images have some artifacts in them due to the fact that they where taken through a thick glass window of the ISS - so there are some reflections happening there...