Digital Terrain From a Two-Step Segmentation and Outlier-Based Algorithm

Abstract

ABSTRACT:We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600km 2 region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2m resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho's Match-T algorithm.

Optimal foraging by a large ungulate in an extreme environment: Wild mountain reindeer select snow‐free feeding habitats in winter

“…The mean value showed a stronger correlation to the ALS‐based snow‐fraction estimates than any of the single scenes (2009 data; Pearson correlation coefficient R = −0.72). From the six Landsat scenes combined with a 1 m digital elevation model (DEM) (Hingee et al., 2016), we also derived the geomorphometric indices Topographic Position Index (TPI) (Newman et al., 2018; window size 30 m), slope, and hill‐shade (depending on illumination angle and direction), averaged to fit the Landsat 30 meter grid size. However, only TPI was kept as the second predictor to model the response snow cover.…”

DTM Correction in Areas of Steep Slopes

“…If no segment does grow anymore, all small segments are eliminated. This algorithm has many variations (Lersch et al, 2004) and successors, to which we also count directional and slope-based approaches (Meng et al, 2009, Hingee et al, 2016, Mousa et al, 2017. Moreover, clustering, gives us a hint to the third criterion as described in (Sithole, Vosselman, 2005), namely that the off-terrain objects must not have too large area.…”

A deep learning approach to DTM extraction from imagery using rule-based training labels

“…For 2D raster methods, slope is often used to define segmentation boundaries. For example, Hingee et al (2016) calculate the slope of the DSM, which is used to segment the raster. Segments where majority of pixels are flowing 'in' are candidates for ground, then surface fitting is applied.…”