is there a use for a changing kernel in image convolution
Yes it's true; there are image filter kernels whose coefficients may change with location of the processing and further based on image data. They're known as shift-varying filters. And data dependency also makes them nonlinear.

One prime application of shift-varying nonlinear filtering is on the edge adaptive, noise reduction where the kernel coefficients are determined based on a statistical analysis of a local group of pixels; according to whether it's an edge dominant or noise dominant block. There are different applications too.