Infrastructure Change Monitoring Using Multitemporal Multispectral Satellite Images

Authors: U. Datta

Abstract:

The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: Co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection.

References:

[1] S. Kotkar and B. Jadhav, "Analysis of various change detection techniques using satellite images," in 2015 International Conference on Information Processing (ICIP), Pune, India, 2015.
[2] I. R. Hegazy and M. R. Kaloop, "Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt," International Journal of Sustainable Built Environment, vol. 4, no. 1, pp. 117-124, 2015.
[3] P. Xiao, X. Zhang, D. Wang, M. Yuan, X. Feng and M. Kelly, "Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition," ISPRS Journal of Phogrammetry and Remote Sensing, vol. 119, pp. 402-414, 2016.
[4] S. Aleksandrowicz, S. Lewinski and Z. Bochenek, "Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries," Remote Sensing, vol. 6, no. 7, pp. 5976-5994, 2014.
[5] A. Stumpf, D. Michea and J. P. Malet, "Improved Co-Registration of Sentinel-2 and Landsat-8 Imagery for Earth Surface Motion Measurement," Remote Sensing, vol. 10, no. 2, pp. 160-180, 2018.
[6] D.LU, P. Mausel, E. Brondizio and E. Moran, "Change detection techniques," International Journal of Remote Sensing, vol. 25, no. 12, pp. 2365-2407, 2004.
[7] A. Schaum, "Clairvoyant fusion: a new methodology for designing robust detection algorithms," in Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 18 Oct. 2016.
[8] M. Guizar-Sicairos, S. T. Thurman and J. R. Fienup, "Efficient subpixel image registration algorithm," OPTICS LETTERS, vol. 33, no. 2, pp. 156-158, 2008.
[9] A. Schaum, "Theoretical foundation of NRL spectral target detection algorithm," Applied Optics, vol. 54, no. 31, pp. 288-296, Sept. 2015.
[10] A. Schaum, "CFAR fusion: A replacement for generalized likelyhood ratio test for Neyman-Pearson problems," IEEE Computer Society, pp. 1-6, 2011.