Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1673-1680, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1673-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1673-1680, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1673-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

ILLUMINATION INVARIANT CHANGE DETECTION (IICD): FROM EARTH TO MARS

X. Wan1,2, J. Liu3, M. Qin1,2, and S. Y. Li1,2 X. Wan et al.
  • 1Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China
  • 2Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing, China
  • 3Earth Science and Engineering Department, Imperial College London, London, UK

Keywords: Illumination, Planetary Surface Change Detection, Phase Correlation, Saliency, Active Contour

Abstract. Multi-temporal Earth Observation and Mars orbital imagery data with frequent repeat coverage provide great capability for planetary surface change detection. When comparing two images taken at different times of day or in different seasons for change detection, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective change detection algorithm therefore has to be robust to the illumination variation. This paper presents our research on developing and testing an Illumination Invariant Change Detection (IICD) method based on the robustness of phase correlation (PC) to the variation of solar illumination for image matching.
The IICD is based on two key functions: i) initial change detection based on a saliency map derived from pixel-wise dense PC matching and ii) change quantization which combines change type identification, motion estimation and precise appearance change identification. Experiment using multi-temporal Landsat 7 ETM+ satellite images, Rapid eye satellite images and Mars HiRiSE images demonstrate that our frequency based image matching method can reach sub-pixel accuracy and thus the proposed IICD method can effectively detect and precisely segment large scale change such as landslide as well as small object change such as Mars rover, under daily and seasonal sunlight changes.