I am working a lot of aerial photos that are very old and were scanned by a contractor. The flight were taken and snapshot over the national forest. The aerial photos are not georeference.
I am going to use them in ERDAS Imagine 2016 AutoSync to perform them with control points and run APM.
The only thing I have some concerns is do I need to have the pixel size to match from the input and the reference ?
For the aerial Photos, what geometic model do I need to use?
No, pixel sizes do not need to match. As your data is not georeferenced anyway, the pixels won't line up after referencing and will be more or less distorted anyway.
But: The better your reference data, the better you'll be able to place your control points!
By "geometric model" I assume you mean which model to use for interpolating between the control points? This is not easily answered. ERDAS Imagine should give you some good pointers in its documentation, already. A simple affine reference is easy and will not distort your source data significantly, but it will have trouble covering many variations that are common in airborne data, especially old one.
Strong variation in airborne data can be caused by movement of the sensor (shaky plane/platform) and vertical variation in terrain. If you have hilly terrain instead of flat, you will need a lot more sample points and distortion to stretch the distorted aerial imagery into place. In these cases, you'll absolutely not be satisfied with an affine model and will need to go for a polynomial one.
It also depends on what accuracy you need for your referencing. Do you intend to do spectral analysis (where you want sub-pixel accuracy) or are you just mapping it roughly for visual reference?
If you have questions regarding specific models and their suitability, expand your question.
All of these questions have an impact on what approach is recommendable to georeference data. Georeferencing will always require human creative input and thus is a bit of an art form (mostly for placing control points), but there's lots of science behind it to put you on the right track.
One last note on polynomial models: The higher polynomial degree, the more it will distort your data, and it will reduce your statistical errors (RMSE). However, it will not necessarily improve your quality of georeferencing, and can even be vastly worse than a polynomial correction of lower degree. Also, high degrees have a vastly higher computational cost.
Ideally you should describe a "camera" model. Try to find information about the camera used on the side of your photograph (if it was entirely scanned, the focal length of the camera and the elevation of the plane are usually visible on the side of old camera, as well as fiducial marks in the corners).
Camera Use this model to create a custom geometric correction model for a specific camera. The Camera model allows orthorectification of any camera data with a single perspective center. It is derived based on colinearity equations. The elevation data is used in the rectification process to remove relief displacement.
If you don't have any information about the camera but you know that it is a row image taken by a camera, then you can use DLT model
Direct Linear Transform (DLT) The Direct Linear Transform (DLT) aids in gathering information as a 3D single perspective. This transformational method is useful for orthocorrecting imagery when there is no interior information available for the imagery, such as when your imagery comes from historical archive film.
If you don't see any information, maybe this is not an old aerial photograph but already an old orthophoto. In this case, affine projection should be used. In fact, you should check that first.
