# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion  
### Pixel Aggregation  
We found SAR coherence methods to be more successful in landslide detection at lower spatial resolutions. There are several potential reasons for this. First, any disagreement in landslide shape or location between the SAR classifier and the validation map will have a negative effect on the ROC analysis since non-overlapping 'landslide' pixels between the two maps result in both false positives and false negatives. Therefore, any problems in geo-referencing in either the coherence maps or the validation inventory will adversely affect classifier performance. This is particularly relevant in the case of Nepal, where [PERSON] et al. (2016) note considerable difficulty in orthorectifying and georeferencing the imagery from which the landslides were mapped. Furthermore, any additional landslides or change in existing landslide shape through landslide reactivation between SAR and optical image acquisition would have the same effect. Reactivation is likely to occur due to unstable ground conditions, rainfall and aftershock activity. The SAR imagery used here was collected 4 days following the earthquake, whereas the optical imagery used by [PERSON] at al. (2016) was acquired over a period of several months. It is also worth noting that as well as landslides, our method may detect other forms of damage, such as  
\begin{table}
\begin{tabular}{c c c c} \hline  & **3 \(\times\) 3** & **5 \(\times\) 5** & **20 \(\times\) 20** \\ \hline Absolute & 0.68 & 0.68 & 0.66 \\ ARIA & 0.69 & 0.69 & 0.64 \\ Bx-S & 0.73 & 0.73 & 0.73 \\ \hline \end{tabular}
\end{table}
Table 1: ROC AUC values for each method for boxcar window sizes 3 \(\times\) 3, 5 \(\times\) 5 and 20 \(\times\) 20. ROC AUC are shown for 200 m \(\times\) 220 m aggregate classifier surfaces.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** \\ \hline Absolute & 0.65 & 0.67 & 0.68 & 0.69 & 0.68 & 0.69 \\ ARIA & 0.65 & 0.65 & 0.67 & 0.68 & 0.69 & 0.69 \\ Bx-S & 0.67 & 0.70 & 0.72 & 0.73 & 0.73 & 0.75 \\ \hline \end{tabular}
\end{table}
Table 2: The effect on ROC AUC of varying the % landslide area threshold required for an aggregate pixel to be defined as ‘landslide’ for 200 m \(\times\) 220 m aggregate pixelscollapsed buildings or damaged roads. Although this may be a useful application of our method for future studies, here this may result in additional false positives, as these areas would not be included in the inventory of individual landslides.  
Second, there are likely to be limitations on the size of an individual landslide that can be detected both by the individual collecting the optical landslide inventory and by the SAR classifier. Of the 24,915 landslides mapped by [PERSON] et al. [2], 6028 (<25%) are under 400 m\({}^{2}\) in area, the size of one SAR pixel, while more than half are smaller than the 3 \(\times\) 3 pixel boxcar window. (Figure 11). The calculation of the boxcar coherence as a spatial average means that sub-pixel landslides are unlikely to be visible and sharp boundaries in coherence are blurred. Using a 3 \(\times\) 3 pixel window, 9 pixels will be used in the summation in Equation (1). For two adjacent pixels, 6 of these will be the same. Sharp coherence changes are therefore spread across the boxcar window [44]. The blurring between neighbouring pixels may mask the signal of small landslides, making pixel scale detections problematic. This affects all three methods, as all use a boxcar coherence estimate to detect landslides. However, this problem is reduced when using SAR coherence to estimate coarser resolution landslide density, since a mean is taken of neighbouring pixels in the aggregation process.