Towards detection of archaeological objects in high-resolution remotely sensed images : the Silvretta case study

We report on recent research undertaken in the framework of the Silvretta Archaeological Project, in which we are developing methods to detect certain types of archaeological ruins in remotely sensed images in order to assist archaeological survey. Our approach aims at assessing the probability of the presence of objects of our interest based on geometric cues that can be automatically detected in the satellite and aerial images that we use. We describe our methodology and the first integral step, constituting a new approach to texture segmentation that we developed to reduce the rate of false detections.

[1]  S. Parcak.  Satellite Remote Sensing for Archaeology , 2009 .

[2]  Karsten Lambers,et al.  Archaeological Prospecting Using High-Resolution Digital Satellite Imagery: Recent Advances and Future Prospects - A Session Held at the Computer Applications and Quantitative Methods in Archaeology (CAA) Conference, Williamsburg, USA, March 2009 , 2009 .

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  David G. Stork,et al.  Pattern Classification , 1973 .

[5]  V. De Laet,et al.  Effects of image characteristics on the identification and extraction of archaeological features from Ikonos-2 and Quickbird-2 imagery: case study Sagalassos (southwest Turkey) , 2009 .

[6]  R. Lasaponara,et al.  Remote Sensing in Archaeology: From Visual Data Interpretation to Digital Data Manipulation , 2012 .

[7]  Marco J. Giardino,et al.  A History of NASA Remote Sensing Contributions to Archaeology , 2013 .

[8]  Bjoern H Menze,et al.  Virtual survey on North Mesopotamian tell sites by means of satellite remote sensing , 2007 .

[9]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[11]  Ø. Trier,et al.  Automatic Detection of Pit Structures in Airborne Laser Scanning Data , 2012 .

[12]  Arianna Traviglia,et al.  Uncovering Angkor: Integrated Remote Sensing Applications in the Archaeology of Early Cambodia , 2012 .

[13]  Pierre Soille,et al.  Advances in mathematical morphology applied to geoscience and remote sensing , 2002, IEEE Trans. Geosci. Remote. Sens..

[14]  A. Beck,et al.  Some techniques for improving the detection of archaeological features from satellite imagery , 2007, SPIE Remote Sensing.

[15]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[16]  David C. Cowley,et al.  In with the new, out with the old? Auto-extraction for remote sensing archaeology , 2012, Remote Sensing.

[17]  Allan Hanbury.  The morphological top-hat operator generalised to multi-channel images , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[18]  Thomas Reitmaier,et al.  Silvretta Historica : Satellite-assisted Archaeological Survey in an Alpine Environment , 2013 .

[19]  Dietmar Saupe,et al.  Morphological operators for segmentation of high contrast textured regions in remotely sensed imagery , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[20]  Carlo Ulivieri,et al.  Automatic archaeological feature extraction from satellite VHR images , 2010 .

[21]  Rune Solberg,et al.  Automatic detection of circular structures in high‐resolution satellite images of agricultural land , 2009 .

[22]  Takeshi Inomata,et al.  Evaluating the use of IKONOS satellite imagery in lowland Maya settlement archaeology , 2008 .