Photogrammetry, together with radar interferometry, is the most popular of the remote sensing techniques used to monitor stability of high mountain slopes. By using two images of an area taken from different view angles, photogrammetry produces digital terrain models (DTM) and orthoprojected images. Repeat digital terrain models (DTM) are differenced to compute elevation changes. Repeat orthoimages are matched to compute the horizontal displacement and deformation of the masses. The success of the photogrammetric approach in the computation of horizontal displacement (and also the generation of DTM through parallax matching, although not covered in this work) greatly relies on the success of image matching techniques.
The area-based image matching technique with the normalized cross-correlation (NCC) as its similarity measure is widely used in mass movement analysis. This method has some limitations that reduce its precision and reliability compared to its theoretical potential. The precision with which the matching position is located is limited to the pixel size unless some sub-pixel precision procedures are applied. The NCC is only reliable in cases where there is no significant deformation except shift in position. Identification of a matching entity that contains optimum signal-to-noise ratio (SNR) and minimum geometric distortion at each location has always been challenging. Deformation parameters such as strains can only be computed from the inter-template displacement gradient in a post-matching process.
To find appropriate solutions for the mentioned limitations, the following investigations were made on three different types of mass movements; namely, glacier flow, rockglacier creep and land sliding. The effects of ground pixel size on the accuracy of the computed mass movement parameters such as displacement were investigated. Different sub-pixel precision algorithms were implemented and evaluated to identify the most precise and reliable algorithm. In one approach images are interpolated to higher spatial resolution prior to matching. In another approach the NCC correlation surface is interpolated to higher resolution so that the location of the correlation peak is more precise. In yet another approach the position of the NCC peak is computed by fitting 2D Gaussian and parabolic curves to the correlation peak turn by turn. The results show that the mean error in metric unit increases linearly with the ground pixel size being about half a pixel at each resolution. The proportion of undetected moving masse increases with ground pixel size depending on the displacement magnitudes. Proportion of mismatching templates increases with increasing ground pixel size depending on the noise content, i.e. temporal difference, of the image pairs. Of the sub-pixel precision algorithms, interpolating the image to higher resolution using bi-cubic convolution prior to matching performs best. For example, by increasing the spatial resolution (i.e. reducing the ground pixel size) of the matched images by 2 to 16 times using intensity interpolation, 40% to 80% of the performances of the same resolution original image can be achieved.
A new spatially adaptive algorithm that defines the template sizes by optimizing the SNR, minimizing the geometric distortion and optimizing the similarity measure was also devised, implemented and evaluated on aerial and satellite images of mass movements. The algorithm can also exclude ambiguous and occluded entities from the matching. The evaluation of the algorithm was conducted on simulated deformation images and in relation to the image-wide fixed template sizes ranging from 11 to 101 pixels. The evaluation of the algorithm on the real mass movements is conducted by a novel technique of reconstructing the reference image from the deformed image and computing the global correlation coefficient and the corresponding SNR between the reference and the reconstructed image. The results show that the algorithm could reduce the error of displacement estimation by up to over 90% (in the simulated case) and improve the SNR of the matching by up to over 4 times compared to the globally fixed template sizes. The algorithm pushes terrain displacement measurement from repeat images one step forward towards full automation.
The least squares image matching (LSM) matches images precisely by modeling both the geometric and radiometric deformation. The potential of the LSM is not fully utilized for mass movement analysis. Here, the procedures with which horizontal surface displacement, rotation and strain rates of glacier flow, rockglacier creep and land sliding are computed from the spatial transformation parameters of LSM automatically during the matching are implemented and evaluated. The results show that the approach computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviation in the order of 10-4 as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the NCC by about 90% in ideal (simulated) case and the SNR of the matching by about 25% in real multi-temporal images of mass movements.
Additionally, advanced spatial transformation models such as projective and second degree polynomial are used for the first time for mass movement analysis in addition to the affine. They are also adapted spatially based on the minimization of the sum of square deviation between the matching templates. The spatially adaptive approach produces the best matching, closely followed by the second-order polynomial. Affine and projective models show similar results closely following the two approaches. In the case of the spatially adaptive approach, over 60% of the entities matched for the rockglacier and the landslide are best fit by the second-order polynomial model.
In general, the NCC alone may be sufficient for low resolution images of moving masses with limited or no deformation. To gain better precision and reliability in such cases, the template sizes can be adapted spatially and the images can be interpolated to higher resolution (preferably not more detail than 1/16th of a pixel) prior to the matching. For highly deformed masses where higher resolution images are used, the LSM is recommended as it results in more accurate matching and deformation parameters. Improved accuracy and precision are obtained by selecting matchable areas using the spatially adaptive algorithm, identifying approximate matches using the NCC and optimizing the matches and measuring the deformation parameters using the LSM algorithm
