Slicing up images to analyze them is definitely something people do - in many cases, such as satellite imagery, there is not much alternative. But it should be

Slicing up images to analyze them is definitely something people do - in many cases, such as satellite imagery, there is not much alternative. But it should be done mindfully, especially if there are differences between the training and testing steps. Depending on the architecture and the application, it's not the same as processing the whole image at once. Some differences are more or less obvious (for example, you might have border artifacts), but others are more subtle. For example, contrary to the expected positional equivariance of convolutional nets, they can implicitly encode positional information based on where they see border padding during training. For some types of normalization such as instance normalization, the statistics of the normalization may vary significantly when applied across patches or whole images.