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A relative density ratio-based framework for detection of land cover changes in MODIS NDVI time series
Citation
Anees, A and Aryal, J and O'Reilly, M and Gale, TJ, A relative density ratio-based framework for detection of land cover changes in MODIS NDVI time series, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, (8) pp. 3359-3371. ISSN 1939-1404 (2016) [Refereed Article]
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Copyright Statement
Copyright 2015 IEEE
DOI: doi:10.1109/JSTARS.2015.2428306
Abstract
To improve statistical approaches for near real-time
land cover change detection in nonGaussian time-series data, we
propose a supervised land cover change detection framework in
which a MODIS NDVI time series is modeled as a triply modulated
cosine function using the extended Kalman filter and the
trend parameter of the triply modulated cosine function is used to
derive repeated sequential probability ratio test (RSPRT) statistics.
The statistics are based on relative density ratios estimated
directly from the training set by a relative unconstrained least
squares importance Fitting (RULSIF) algorithm, unlike traditional
likelihood ratio-based test statistics. We test the framework
on simulated, synthetic, and real-world beetle infestation datasets,
and show that using estimated relative density ratios, instead
of assuming the individual density functions to be Gaussian or
approximating them with Gaussian Kernels, in the RSPRT statistics
achieves better performance in terms of accuracy and detection
delay. We verify the efficiency of the proposed approach by
comparing its performance with three existing methods on all the
three datasets under consideration in this study. We also propose
a simple heuristic technique that tunes the threshold efficiently in
difficult cases of near real-time change detection, when we need
to take three performance indices, namely, false positives, false
negatives, and mean detection delay, into account simultaneously.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | change detection, extended Kalman filter (EKF), model fitting, MODIS, relative density ratio, time series |
| Research Division: | Engineering |
| Research Group: | Geomatic Engineering |
| Research Field: | Photogrammetry and Remote Sensing |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding Knowledge |
| Objective Field: | Expanding Knowledge in Engineering |
| UTAS Author: | Anees, A (Mr Asim Anees) |
| UTAS Author: | Aryal, J (Dr Jagannath Aryal) |
| UTAS Author: | O'Reilly, M (Dr Malgorzata O'Reilly) |
| UTAS Author: | Gale, TJ (Dr Timothy Gale) |
| ID Code: | 100684 |
| Year Published: | 2016 (online first 2015) |
| Web of Science® Times Cited: | 7 |
| Deposited By: | Mathematics and Physics |
| Deposited On: | 2015-05-26 |
| Last Modified: | 2017-10-25 |
| Downloads: | 74 View Download Statistics |
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