Abstract
Image change detection is established for extracting changed regions in multiple images of the same scene captured at different times. Recent research has demonstrated that the change detection methodologies using satellite images, such as multi-temporal visible light remote sensing image and synthetic aperture radar (SAR) image, are particularly useful for damage assessment after various disasters, e.g., earthquakes, fires, floods, and landslides. The level set method, because of its implicit handling of topological changes and low sensitivity to noise, is one of the most effective unsupervised change detection techniques for satellite images. The signed pressure force function (SPF) improved the performances of conventional level set methods through including two grayscale parameters, i.e., the average pixel intensity inside and outside the contour, respectively. However, the mean of region pixel intensity is not a good indicator in case that the images are inhomogeneities grayscale, e.g., confused-edge objects in satellite images. In order to address this problem, we propose a novel model, denoted as dynamic SPF (D-SPF) model, which can dynamically learn a discriminative indicator for distinguishing the pixels inside or outside the contour. Specifically, the principle of maximizing entropy between the regions inside and outside the contour is used to learn the K distinguish parameters, which help to guide the segmentation contour to end on the object’s edge. The experiments are conducted on a public satellite image dataset, i.e., ERS, which contain 670 SAR and 670 optical images; each image covers approximately 150 m2 area including forests, lakes, and cities, etc. These images are challenging due to the inhomogeneities of landforms and unknown natural disasters. The experimental results demonstrate that D-SPF model reduces almost 30.4% missed detection rate on the optical images and 41.2% missed detection rate on the SAR images in comparison with SPF and obtains the best detection performances in ERS dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Singh A (1989) Review article digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003
Bazi Y, Melgani F, Al-Sharari HD (2010) Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Trans Geosci Remote Sens 48(8):3178–3187
Celik T, Ma KK (2011) Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Trans Geosci Remote Sens 49(2):706–716
Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27
Pun T (1981) Entropic thresholding, a new approach. Comput Graph Image Proc 16(3):210–239
Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE computer society conference on Computer Vision and Pattern Recognition CVPR 2005, IEEE, vol 1, pp 430–436
Chan Tony F, Luminita A (2001) Active contours without edges. IEEE Trans Pattern Anal Mach Intell 10(2):266–277
Caselles V, Morel J, Sapiro G (1997) Geodesic active contours. Int J Comput Vision 22:61–79
Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. Int J Comput Vision 1(4):321–331
Zhang K, Song H, Zhang L, Zhou W (2010) Active contours with selective local or global segmentation: A new formulation and level set method. J Image Vis Comput 28(4):668–676
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, Y., Li, Y., Lu, Y., Liu, J. (2020). Remote Sensing Image Change Detection and Location Based on Dynamic Level Set Model. In: Jing, Z. (eds) Proceedings of the International Conference on Aerospace System Science and Engineering 2019. ICASSE 2019. Lecture Notes in Electrical Engineering, vol 622. Springer, Singapore. https://doi.org/10.1007/978-981-15-1773-0_22
Download citation
DOI: https://doi.org/10.1007/978-981-15-1773-0_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1772-3
Online ISBN: 978-981-15-1773-0
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)