Basic queries for Object Detection using deep learning
The input to your CNN is a small cropped image, and the output will be a prediction at the label (either "airplane" or "not-airplane").  The training set needs to contain example input-output pairs.  So, you need your training set to contain many small images (of the size of a single crop), with a label indicating whether it should be classified as "airplane" or "not-airplane".

How do you do that?  You take many large images where you know where the bounding box of the airplanes in each is.  For each large image, you crop it around an airplane and add that to the training set, labelled as "airplane".  You also crop it somewhere else (in a place not intersecting any of the airplane bounding boxes) and that to the training set, labelled as "not-airplane".  You can do the letter step many times, taking many different crops.  Repeat this for many large images.  Use the resulting training set to train your neural network.