Assuming a correct use of the instruments and the post‐processing computer tools, the MDEs generated with these data will have high accuracy
indirect methods
photogrammetry
digitalization of contour lines or spot heights
massive points
on-ground surveys
laser scanners
passive and active aerial and satellite sensors
LIDAR or RADAR altimeter
indirect methods
cross-correlation photogrammetric methods
comparison of digital images from stereoscopic pairs of digitized aerial photographs
There is a wide variety in DEM accuracy depending on the flight's height and focal length of the camera, the accuracy in field support, the aerotriangulation method used, the resolution when scanning photos, the operator experience, and the instruments used.
Structure from Motion
Photogrammetric techniques that allow, starting from conventional photographs not calibrated:
obtain the position of cameras and the angles of correlations
obtain maps of disparity (paralax)
get a cloud of high density points
generate DSM
final objective is to determine a 3D model of the terrain
RADAR interferometry
digital cartography
algorithms for SfM
SIFT (Scale Invariant Feature Transform)
for detection of invariant points
It aims to find relevant features in an image by analyzing the invariance in scale, rotation and position
We extract the elements in the spatial and frequency domain that have invariance
For each obtained point a descriptor is generated and is used to match the elements found in each one of the individual images
By mapping points between images, the 3D position of the point is determined
detection of endpoints in the space of scales → location of the characteristic points → allocation of dominant orientation, based on the local gradients of the image → descriptor generation
Descriptor SURF (Speeded-up robust features)
Enhanced computational performance using a Hessian matrix and an integral image descriptor. Is several times faster than SIFT and more robust against different image transformations than SIFT
ASIFT (assine SIFT)
Determines invariant points with SIFT when performing affine transformations
PCA-SIFT
Variation of SIFT against changes of illumination and that reduces the dimensionality of the feature vectors
GLOH (Gradient Location‐Orientation Histogram)
Calculate SIFT for the regions resulting from 3 divisions in the radial direction and 8 in the angular direction
Permalink how_to_create_dem.txt · Last modified: 2017/05/15 20:49 by efox