# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 2 Methods
### Automated ICC Procedure  
The procedure presented here automatically selects images on which to perform ICC and obtains displacement maps of geophysical phenomena. This procedure was developed in 2018 and its effectiveness was tested in the monitoring of the Plampincieux glacier on the Italian side of the Mont Blanc massif. The processing chain is designed for automatic, continuous, and autonomous monitoring via ICC in the typical situation of daily acquisitions. The procedure is best suited for surveying natural phenomena at velocities greater than the common ICC uncertainty, i.e., \(\sim\)10\({}^{-1}\) px [12; 13]. It can thus be applied to observe relatively fast natural processes such as glacier flows and slope instabilities that are quite active. Depending on the phenomenon monitored, it is possible to vary the scheduled image acquisition and processing frequencies.  
The procedure is conducted on a daily frequency and it is divided into four independent subroutines (Figure 2).  
1. Image acquisition. First, the images are acquired at an hourly frequency by the monitoring system and are then transferred in real time to the data collection section of the operative server.
2. Image classification and selection. The images acquired in the current day are classified through SVM according to the visual and illumination conditions. It is attributed a posteriori probability to belong to the classes. The results are saved in a list of classified images, and this list is subsequently browsed to search for the image with the highest probability score to belong to the DiffLight class. Such an image is labelled as suitable for ICC. If there are no new images, a warning is released and the process skips to the next subroutine.  
Figure 1: (**a**) Original 8-bit trichromatic (RGB) image of NoVis class; (**b**) saturation map; (**c**) hue map; (**d**) 8-bit RGB values, and (**e**) intensity histogram in which the ordinate value of the black triangle indicates the _maxPeak_s and the abscissa value of the white triangle represents _posPeak_s features. The dark and light grey areas are _blackNum_ and _greyNum_ features, respectively.  
3. Image coregistration. This subroutine coregisters the new images acquired. A common reference image is used to coregister all the images and the coregistered images are saved. Such images are used for ICC and for possible manual vision inspection.
4. Image cross-correlation. The list of images suitable for ICC is browsed to check for possible new image couples. If identified, the new images are cross-correlated and displacement maps are computed.  
To evaluate the performance of the automated image selection, we manually classified the images and selected the images suitable for ICC. On several occasions (i.e., approximately 10% of cases), manual classification of images was difficult, and there was a certain ambiguity between the classes. Therefore, three new bivalent classes were introduced relating to images that could be arbitrarily classified as either SunLight or DiffLight, SunLight or NoVis, and DiffLight or NoVis (respectively SD, SN, DN).  
Moreover, the performances of automated selection through SVM were then compared with other possible simpler alternatives of automatic criteria. We analysed the cases of selecting images at a fixed hour of the day: At 12:00 (the SunLight class) and at 18:00 (the DiffLight class). To simulate such automated selection methods, photos were picked without applying quality control to verify whether the scene was adequately visible.