Atmospheric waves are never perfectly monochromatic and show modulations in amplitude, wavelength, and frequency due to transient background conditions. In observations, they often appear as complex superpositions of multiple interacting wave components. To disentangle this superposition into individual interpretable wave packets and to study their instantaneous spectral properties in an automatic fashion, we introduce a new method: the Wavefield Analysis and Segmentation unraveling an interpretable set of wave packets (WAVEGUISE). Based on the continuous wavelet transform, WAVEGUISE combines watershed segmentation from image processing and density based spatial clustering algorithms to partition the wavelet domain into coherent regions of shared temporal, spatial, and spectral characteristics. We demonstrate the method using three observational data sets of increasing dimensionality: a) a 25-day pressure time series (1-D) from a long-duration super-pressure balloon obtained during the Strateole-2 campaign, b) a 1,400 × 500 km Q-line radiance swath (2-D) from the Atmospheric Waves Experiment (AWE), and c) a OH (3,1) band intensity video sequence (3-D) from the Advanced Mesospheric Temperature Mapper. Applied to these data sets, WAVEGUISE acts as a magnifying glass. The segmentation into a set of dominant and subdominant interpretable wave packets reveals a significantly enhanced level of detail enabling to precisely study dynamical processes such as e.g. wave-wave interaction and to improve wave statistics.