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Satellite stereo imagery is becoming a popular data source for derivation of height information. Many new Digital Surface Model(DSM) generation and evaluation methods have been proposed based on these data. A novel Digital Terrain Model (DTM)extraction method based on the DSM from satellite stereo imagery is proposed in this paper. Instead of directly filtering theDSM, firstly a single channel based classification method is proposed. In this step, no multi-spectral information is used, becausefor some stereo sensors, like Cartosat-1, only panchromatic channels are available. The proposed classification method adopts therandom forests method to get initial probability maps of the four main classes in forest regions (high-forest, low-forest, ground,and buildings). To cover the pepper and salt effect of this pixel based classification method, the probability maps are furtherfiltered based on the adaptive Wiener filtering. Then a cube-based greedy strategy is applied in generating the final classificationmap from these refined probability maps. Secondly, the height distances between neighboring regions are calculated along theboundary regions. These height distances can be used to estimate the relative region heights. Thirdly, the DTM is extracted bysubtracting these relative region heights from the DSM in the order of: buildings – low forest – high forest. In the end, theextracted DTM is further smoothed using median filter.  The proposed DTM extraction method is finally tested on satellite stereo imagery captured by Cartosat-1. Quality evaluation isperformed by comparing the extracted DTMs to a reference DTM, which is generated from the last return airborne laser scanningpoint cloud

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

DTM generation in forest regions from satellite stereo imagery