It has been demonstrated that passive MMW imagers can be used to detect obstacles through the fog, such as treelines and hillsides, which might be encountered in the path of a low-flying aircraft. However, the brightness temperature contrast between the horizon sky and the obstacle can often be quite small in foggy conditions, on the order of 5 K or less. Reliable detection of this contrast without image processing requires a passive MMW imager with a Δ-Tmin of about 0.2 K, which is quite challenging for existing 30-Hz imagers. While improvements in passive MMW imagers continue, it is useful to look at image analysis techniques that have the potential to improve obstacle detection by increasing the amount of information extracted from each image frame. In this paper we look at the ways that texture can be used to extract more information from the imagery. By merging textural information with the brightness temperature contrast information, there is the potential to enhance the detection of objects within the scene. The data used for the analysis presented here is 93-GHz, passive imagery of a deciduous treeline scene and a concrete building scene. The data were taken from the roof of a 4-story building to simulate the view of a low-flying aircraft. The data were collected over many months with an ARL-built Stokes-vector radiometer. This radiometer is a single-beam system that raster scans over a scene to collect a calibrated 93-GHz image. Texture measurement results for image segment samples, including autocorrelation and spatial edgeness, are presented in this work. Also presented are the effects of applying a modified Sobel edge detection technique to imagery with the least detectable obstacles.