An unsupervised approach based on Riemannian metric to change detection on multi-temporal SAR images

Na Li; Fang Liu; Zengping Chen

doi:10.1117/12.2066969

23 October 2014 An unsupervised approach based on Riemannian metric to change detection on multi-temporal SAR images

Na Li, Fang Liu, Zengping Chen

Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 924418 (2014) https://doi.org/10.1117/12.2066969
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

This paper investigates the problem of detecting changes on multitemporal SAR imagery in an unsupervised way. A novel change indicator was developed to identify the temporal changes. It is computed by the local average of the amplitude ratio comparing the exponentiation of the local average of the logarithm–transformed amplitude ratio. Compared with the classical ratio of local means, the novel operator is more effective in identifying the changed pixels even the local means are preserved. The classification is implemented by an automatic thresholding algorithm derived from a new Riemannian metric defined in the differential geometry structure. The geodesic distance derived from the new Riemannian metric provides a way to compare the distance between the probability distributions of the changed class and the non-changed class. The probability density functions of the changed and non-changed classes are characterized over the photometric variable. By maximizing the distance between the probability density distributions of the two classes, the misclassification errors are minimized and the optimal threshold is achieved accordingly. Experiments were carried on portions of multi-temporal Radarsat-1 SAR data. The obtained accuracies confirm the effectiveness of the proposed approach.

Citation Download Citation

Na Li, Fang Liu, and Zengping Chen "An unsupervised approach based on Riemannian metric to change detection on multi-temporal SAR images", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 924418 (23 October 2014); https://doi.org/10.1117/12.2066969

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