Phase correlation vs. normalized cross-correlation
I came across this conundrum in a 1 dimensional case, which is how I will present it.  Consider two signals that you want to correlate.  Signal 1 (figure panel a) is a damped sine wave and signal 2 (figure panel b) is two instances of signal 1 but at slightly different amplitudes.  



Now consider using a normalized cross correlation as you defined in your question (figure panel c).  The result is a smoothly oscilating function that has a peak amplitude when the peak from signal 1 and the peak from signal 2 are aligned.  Conversely the normalized cross correlation function has troughs when the peak from signal 1 lines up with the troughs from signal 2.    

Then consider using a phase correlation as you defined in your question (figure panel d).  A phase correlation involves a division of the absolute value (or amplitude) of the two signals.  In a 1D case that is a vector and in a 2d case, as your question is, that is a matrix.  In either case the values in that divisor can be very close to zero and when you divide by something close to zero, you get spikes or discontinuities.  

There are several strategies around this and include low pass filtering the input signals or the phase correlated function.  This page on stack overflow might be helpful: https://stackoverflow.com/questions/30630632/performing-a-phase-correlation-with-fft-in-r