This article studies the use of airborne global navigation satellite system reflectometry techniques for remote sensing applications at regional scale. The objective is to classify the reflectivity of airborne global navigation satellite system (GNSS) signals in order to differentiate various reflective surfaces along the satellite traces. For this purpose, we propose a segmentation algorithm based on an online change point detector and an offline change point localization estimate. Given the presence of speckle noise in GNSS signals, a homomorphic log-transformation is applied to mitigate this noise. In this context, the cumulative sum change point detector and the maximum likelihood change point localization are designed for a log-gamma distribution. We show that the proposed radar segmentation system is able to automatically detect different landforms along real flight experiments that took place in the northern region of France. Automatic classification using K-means clustering is applied to the segmented signals allowing to distinguish different segments of the signal.