Horizon Line Detection in Historical Terrestrial Images in Mountainous Terrain Based on the Region Covariance

Sebastian Mikolka-Flöry,Norbert Pfeifer

doi:10.3390/rs13091705

Abstract

Horizon line detection is an important prerequisite for numerous tasks including the automatic estimation of the unknown camera parameters for images taken in mountainous terrain. In contrast to modern images, historical photographs contain no color information and have reduced image quality. In particular, missing color information in combination with high alpine terrain, partly covered with snow or glaciers, poses a challenge for automatic horizon detection. Therefore, a robust and accurate approach for horizon line detection in historical monochrome images in mountainous terrain was developed. For the detection of potential horizon pixels, an edge detector is learned based on the region covariance as texture descriptor. In combination with shortest path search the horizon in monochrome images is accurately detected. We evaluated our approach on 250 selected historical monochrome images in average dating back to 1950. In 85% of the images the horizon was detected with an error less than 10 pixels. In order to further evaluate the performance, an additional dataset consisting of modern color images was used. Our method, using only grayscale information, achieves comparable results with methods based on color information. In comparison with other methods using only grayscale information, accuracy of the detected horizons is significantly improved. Furthermore, the influence of color, choice of neighborhood for the shortest path calculation, and patch size for the calculation of the region covariance were investigated. The results show that both the availability of color information and increasing the patch size for the calculation of the region covariance improve the accuracy of the detected horizons.

Highlights

For the CH1 dataset our approach is compared with the results of various methods reported in [14,16,32,33]: DCNN-DS1: Fully convolutional network proposed in [16]; support-vector machine (SVM): Patch classification using normalized gray-scale values [14]; CNN: Convolutional neural network trained on patches proposed in [14]; Structured Forest: Learned edge detection proposed in [32]; Random Ferns: Learned edge detection proposed in [33]
SVM is most similar to our approach as it is based on normalized pixel intensities extracted from 16 × 16 patches
Comparison with [14] on the CH1 dataset shows the discriminative power of the region covariance matrix

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Image archives hold impressive collections of historical terrestrial images from the Alpine regions. These images are an important, yet seldom used, source for identifying and documenting changes in the alpine landscape. Taken by early mountaineers without any auxiliary devices (e.g., global navigation satellite systems), the accurate position and orientation of these images are unknown. The quantification of visible changes by monoplotting [1] becomes possible. Instead of relying on missing or wrong metadata, georeferenced images can be queried by their estimated location

Methods

Results

Discussion

Conclusion