Standard filtering techniques perform well on digital surface models (DSMs) generated from light detection and ranging
(LiDAR) data. However, these techniques have to be tested and evaluated using point clouds obtained by matching of stereo
satellite imagery. This paper proposes a new iterative filtering technique based on fuzzy c-means (FCM) clustering. This method
is composed of three main steps: (1) a DSM is generated from GeoEye-1 stereo pair imagery, (2) the generated DSM is then
reshaped and applied as input data for a FCM clustering process to separate terrain and non-terrain points, and (3) terrain points
are then interpolated into a grid digital terrain model (DTM). An urban test area with distinct land use/cover classes covering the
north-east part of Cairo city in Egypt has been selected. To evaluate the performance of the proposed method, the filtered DTM
was compared against reference data that was generated manually, and type I, type II, and total errors were estimated. Compared
with the most commonly used filtering method, progressive triangular irregular network densification (PTD), the proposed
approach has identified terrain points much closer to the reference DTM with less variable accuracies. The FCM filter has
reduced the mean errors by 2.06, 0.74, and 0.34% for type I, type II, and total errors, respectively. On the other hand, the mean
standard deviation (SD) of the differences between the obtained and reference DTMs has been reduced by 0.38 m.