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Optimal utilization of complex synthetic aperture radar imagery for coherent change detection (CCD) is achieved by maximizing the amount of information extracted from the coherent correlation of images. Conventional techniques cannot fully exploit the coherent information due to limited application of few products or indicators, e.g., correlation factor and phase maps. Also, considering the lack of a systematic formulation of change observables and their nature, unsupervised change detection or classification is not feasible. To address this, an analytic framework is established by taking advantage of the analogy to partially polarized electromagnetic fields to introduce vectors and observables that can establish a complete change space. Decomposition of the coherent correlation or change characteristics into this basis set can provide a better understanding of the associated change phenomenology.