TY - JOUR

T1 - Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes

AU - Kruse, Christian

AU - Wittich, Dennis

AU - Rottensteiner, Franz

AU - Heipke, Christian

N1 - Funding Information: Parts of this work were financially supported by the State Office for Geoinformation and Surveying of Lower Saxony, Germany, and its Explosive Ordnance Disposal Service (KBD) as a department of the regional directorate Hamelin-Hanover, as well as the EU-project ‘VOLTA – innoVation in geOspatiaL and 3D daTA’ funded under the Marie Skłodowska-Curie RISE scheme (REA Grant Agreement no. 734687). The authors would also like to thank the KBD, the 3D Optical Metrology research unit of the Bruno Kessler Foundation (FBK) in Trento, Italy and SAGIS of the Federal State of Salzburg, Austria, for providing the data.

PY - 2022/8

Y1 - 2022/8

N2 - Even more than 75 years after the Second World War, numerous unexploded bombs (duds) linger in the ground and pose a considerable hazard to society. The areas containing these duds are documented in so-called impact maps, which are based on locations of exploded bombs; these locations can be found in aerial images taken shortly after bombing. To generate impact maps, in this paper we present a novel approach based on marked point processes (MPPs) for the automatic detection of bomb craters in such images, some of which are overlapping. The object model for the craters is represented by circles and is embedded in the MPP-framework. By means of stochastic sampling, the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function that describes the consistency with a predefined object model. High gradient magnitudes along the object borders and homogeneous grey values inside the objects are favoured, while overlaps between objects are penalized. Reversible Jump Markov Chain Monte Carlo sampling, in combination with simulated annealing, provides the global optimum of the energy function. Our procedure allows the combination of individual detection results covering the same location. Afterwards, a probability map for duds is generated from the detections via kernel density estimation and areas around the detections are classified as contaminated, resulting in an impact map. Our results, based on 74 aerial wartime images taken over different areas in Central Europe, show the potential of the method; among other findings, a clear improvement is achieved by using redundant image information. We also compared the MPP method for bomb crater detection with a state-of-of-the-art convolutional neural network (CNN) for generating region proposals; it turned out that the CNN outperforms the MPPs if a sufficient amount of representative training data is available and a threshold for a region to be considered as crater is properly tuned prior to running the experiments. If this is not the case, the MPP approach achieves better results.

AB - Even more than 75 years after the Second World War, numerous unexploded bombs (duds) linger in the ground and pose a considerable hazard to society. The areas containing these duds are documented in so-called impact maps, which are based on locations of exploded bombs; these locations can be found in aerial images taken shortly after bombing. To generate impact maps, in this paper we present a novel approach based on marked point processes (MPPs) for the automatic detection of bomb craters in such images, some of which are overlapping. The object model for the craters is represented by circles and is embedded in the MPP-framework. By means of stochastic sampling, the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function that describes the consistency with a predefined object model. High gradient magnitudes along the object borders and homogeneous grey values inside the objects are favoured, while overlaps between objects are penalized. Reversible Jump Markov Chain Monte Carlo sampling, in combination with simulated annealing, provides the global optimum of the energy function. Our procedure allows the combination of individual detection results covering the same location. Afterwards, a probability map for duds is generated from the detections via kernel density estimation and areas around the detections are classified as contaminated, resulting in an impact map. Our results, based on 74 aerial wartime images taken over different areas in Central Europe, show the potential of the method; among other findings, a clear improvement is achieved by using redundant image information. We also compared the MPP method for bomb crater detection with a state-of-of-the-art convolutional neural network (CNN) for generating region proposals; it turned out that the CNN outperforms the MPPs if a sufficient amount of representative training data is available and a threshold for a region to be considered as crater is properly tuned prior to running the experiments. If this is not the case, the MPP approach achieves better results.

KW - Aerial wartime images

KW - Bomb craters

KW - Duds

KW - Impact maps

KW - Marked point processes

KW - Reversible Jump Markov Chain Monte Carlo sampling

UR - http://www.scopus.com/inward/record.url?scp=85182611595&partnerID=8YFLogxK

U2 - 10.1016/j.ophoto.2022.100017

DO - 10.1016/j.ophoto.2022.100017

M3 - Article

AN - SCOPUS:85182611595

VL - 5

JO - ISPRS Open Journal of Photogrammetry and Remote Sensing

JF - ISPRS Open Journal of Photogrammetry and Remote Sensing

SN - 2667-3932

M1 - 100017

ER -