Geostatistical quantification of uncertainty in change detection based on differencing spatially aggregated means

Jingxiong Zhang; Deren Li; Jianya Gong

doi:10.1117/12.655276

3 November 2005 Geostatistical quantification of uncertainty in change detection based on differencing spatially aggregated means

Jingxiong Zhang, Deren Li, Jianya Gong

Author Affiliations +

Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604421 (2005) https://doi.org/10.1117/12.655276
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Nowadays, large quantity of data at faster repeatability is generated from various remote sensors and prompts for spatio-temporally integrated strategies for data handling and information extraction. Change detection is one of the essential techniques for near real-time analysis in remote sensing of the environment. Assuming overall phonological conditions being comparable, change detection is performed either on two-point timescale (bi-temporal) or on a continuous timescale (temporal trajectory analysis), with the latter having the advantage of minimizing the influence of phenology. Univariate image differencing is the most widely applied change detection algorithm, which involves subtracting one date of imagery from a second date that has been co-registered to the first. With "perfect" data, positive and negative values would represent areas of change in the resultant difference imagery, and zero values representing no change. To quantify the uncertainty in remotely sensed change detection, a geostatistical framework is proposed so that the mean and standard error in pixel or parcel-based difference between the means of the bi-temporal image/map subsets are computed with spatial and temporal dependence accounted for properly, paving the way for probabilistic mapping of changes. To make the proposed approach adaptable to both regular and irregular sampling schemes, block co-kriging is formulated to evaluate means and standard errors in the differences between spatially aggregated means. The geostatistical framework for uncertainty mapping in bi-temporal image/map-based change detection is tested using simulated data sets, whose spatial and temporal correlation can be prescribed. It is anticipated that the geostatistical approach advocated in this paper will make valuable addition to the literature on spatial uncertainty in remote sensing and change detection.

Citation Download Citation

Jingxiong Zhang, Deren Li, and Jianya Gong "Geostatistical quantification of uncertainty in change detection based on differencing spatially aggregated means", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604421 (3 November 2005); https://doi.org/10.1117/12.655276

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available