Complex-valued region-based-coupling image clustering (continuous soft segmentation) neural networks are proposed for interferometric radar image processing. They deal with the amplitude and phase information of radar data as a combined complex-amplitude image. Thereby, not only the reflectance but also the distance (optical length) are consistently taken into account for the clustering process. A continuous complex-valued label is employed whose structure is the same as that of input raw data and estimation image. Experiments demonstrate successfully the clustering operations for interferometric synthetic aperture radar (InSAR) images. The method is applicable also to future radar systems for image acquisition in, e.g., invisible fire smoke places and intelligent transportation systems by generating a processed image more recognizable by human and automatic recognition machine.