High resolution imaging is a persistent goal for many users of optical and microwave remote sensing data as it allows better detection, discrimination and classification of the objects contained within a recorded scene. High resolution can be obtained either by instrument design, by data acquisition methods, or by dedicated post-processing and interpretation of the acquired data. The interpretation can be based on a visual inspection by a human photointerpreter, or consist of automated model-based scene understanding. We will compare various methods used for optical and SAR instruments and their processing chains. Primary criteria for high resolution imaging as encountered in remote sensing of solid surfaces are ground resolution per pixel, motion compensation during data acquisition, attainable contrast and signal-to-noise ratio, removal of instrumental and non-target effects, geometrical correction, use of multi-channel and neighborhood target property data like spectral and textural signatures, and the potential for data fusion from multiple sources. The inclusion of model knowledge obtained from collections of pre-recorded physical target data leads to a comparison of the acquired data with representative models. Similarities and deviations revealed during the comparison allow a detailed high resolution interpretation of the image data and lead to a full image understanding.