For road implanted explosive hazard detection, detecting the road at distance is critical for the classification algorithms and for sensor positioning to maintain road view during turns. In this paper, we propose the use of the Log-Gabor Filter (LGF) to enhance our road detection system. The LGF can be used to suppress the road-like pixels in the image. By filtering the unpaved road images with varying scales and orientations of the LGF and a combination of basic image processing techniques, evidence images of the road are created. Each evidence image is a binary image where value one at any pixel represents evidence of the road at that pixel. Otherwise the value will be zero. However, the maximum distance for generating evidence of the road varies for each image. Therefore, additionally, a road model is utilized. Using the least squares algorithm, the road model is optimized to fit the support of the road presented in each image. By specifying the length of the road on the optimized model, the distance of road detection can also be specified. Thus, utilizing the LGF and the road model allows our system to detect poorly defined dirt roads as far as forty meters as shown for a winding road at an arid U.S. Army test site.