Smoothing filters and segmentation algorithms

Between the SAGA and OTB toolboxes, there were six different smoothing filters available for consideration: SAGA offers the options exponential, nonlinear, and Gaussian while OTB provides Mean, Large Scale Means Shift (LSMS), Gaussian and Anisotropic Diffusion (Anidif) smoothing filters.

The author found no information on the type of exponential and nonlinear filters that are offered by SAGA. OTB on the other hand offers Anisotropic Diffusion as a specific type of nonlinear filter and Gaussian smoothing is a type of exponential filter. For this reason, it was decided to consider only the smoothing algorithms provided by OTB.

Gaussian and Mean smoothing are filters that blur the images to remove not only noise but also detail (Figure xxv).28 By contrast, LSMS smoothing and Anidif are smoothing algorithms specifically designed to reduce image noise without removing edges, lines or other image details. Because of this quality, these two filters were deemed suitable for the study area in the hopes that the terrace edges would be left largely unchanged but with a general noise reduction in the areas around them.

Figure xxv: The results of the Mean (left) and Gaussian (right) smoothing algorithms led to very blurred visualisations (DTM: Bundesamt für Landestopografie (2019), visualisation created in RVT2.2.1 (Kokalj and Somrak 2019) by Pierina Roffler).

For testing purposes, a regular, unfiltered slope visualisation as well as the LSMS and Anidif smoothing filters were applied, followed by segmentation and classification. For testing purposes, all three images were segmented both with a Watershed and a Meanshift segmentation algorithm.

Simply put, Watershed segmentation works by simulating the flooding of the image, recording local maxima and minima as the water pools at the minima. In order to counteract an over segmentation, a depth threshold can be defined which establishes the minimum depth of a pool, thus combining local minima together until this minimum depth is achieved. By experimentation, it was found that for the Watershed segmentation, leaving all parameters to default except for the depth threshold, which was changed to 0.05, led to the best segmentation results (figure xxvi). “Best” in this case means that some of the segments are merged to larger regions without reaching a state of under segmentation. The image is still largely over segmented, but an over segmentation is much easier to deal with than an under segmentation.

Meanshift segmentation is a region growing algorithm that replaces each pixel with the mean of pixels in a predefined neighbourhood range, and this neighbourhood must be within a predefined distance of the pixel. For the Meanshift segmentation,

it was found that leaving all values to default except the range radius, which was changed to 1, and the spatial or distance radius, which was changed to 50, led to the best results (Figure xxvi)29. Again, “best” in this case means the largest segments possible without the image being under segmented. While the Meanshift segmentation may seem like the clearer choice compared to the Watershed segmentation due to the larger size of segments, upon closer consideration it was found that some segments of the Meanshift segmentation results span multiple objects (Figure xxvi). An example of a segment that spans multiple terrace slopes and flats is indicated in yellow. It was thus decided to work with the Watershed segmentation in the final workflow.

Figure xxvi: The results of the Watershed (left) and Meanshift (right) segmentation. An apparent over segmentation in the Watershed segmentation is needed for exact training segments. One of the meanshift segments that spans multiple terrace flats and slopes is highlighted in yellow (DTM: Bundesamt für Landestopografie (2019), visualisation created in RVT2.2.1 (Kokalj and Somrak 2019) by Pierina Roffler).

For both segmentation options as well as the two smoothed and a regular, unsmoothed slope visualisation, the training and classification procedure was the same. The attribute table of the segmentation layers was opened, the default segment-ID column was deleted and a new column for the training data added. Then, segments that represented the terrace edge and terrace flats were selected and given either class 1 (flat) or class 2 (slope). Next, the SAGA toolbox algorithm

29 _{QGIS Layers “slope cropped”, “segments regular watershed”, “segments regular meanshift” and} “ra85terrace”.

“Semi automatic classification for grids” was used and the segmentation layer was selected as training data. This led to 6 different classifications (table viii).

It was decided based on these six classification results that the unfiltered visualisation along with the Watershed segmentation led to the most accurate results. Smoothing filters lead to more homogeneous, less pixelated results but the author realised that this comes at the cost of a loss in accuracy. For this reason, the final workflow makes use of the Watershed segmentation of an unfiltered visualisation input and uses the attribute table to directly input training classes.

Table viii: Classification of the terraces in the upper study area. In light green, potential terrace flats and in dark green potential terrace slopes are classified. The terrace edges that were mapped by Angelika Abderhalden- Raba are overlaid in blue (Raba 1996).

Unfiltered visualisation, based on Watershed segmentation30

Unfiltered visualisation, based on Meanshift segmentation31

30 _{QGIS layers “regular watershed classification no roads” and “ra85terrace”.} 31 _{QGIS layers “regular meanshift classification“ and “ra85terrace”.}

Visualisation filtered with Anidif smoothing algorithm, based on Watershed segmentation32

Visualisation filtered with Anidif smoothing algorithm, based on Meanshift segmentation33

Visualisation filtered with LSMS smoothing algorithm, based on Watershed segmentation34

Visualisation filtered with LSMS smoothing algorithm, based on Meanshift segmentation35