Change Detection with Kalman Filter and CUSUM

2006 ◽  
pp. 243-254 ◽  
Author(s):  
Milton Severo ◽  
João Gama
Keyword(s):  
Kalman Filter ◽  
Change Detection

scholarly journals A Near Real-Time Method for Forest Change Detection Based on a Structural Time Series Model and the Kalman Filter

2020 ◽  
Vol 12 (19) ◽  
pp. 3135 ◽  
Author(s):  
Martin Puhm ◽  
Janik Deutscher ◽  
Manuela Hirschmugl ◽  
Andreas Wimmer ◽  
Ursula Schmitt ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Change Detection ◽  
Real Time ◽  
Detection Method ◽  
Time Series Model ◽  
Forest Change ◽  
Vegetation Monitoring ◽  
Structural Time Series ◽  
Sentinel 2

The increasing availability of dense time series of earth observation data has incited a growing interest in time series analysis for vegetation monitoring and change detection. Vegetation monitoring algorithms need to deal with several time series characteristics such as seasonality, irregular sampling intervals, and signal artefacts. While common algorithms based on deterministic harmonic regression models account for intra-annual seasonality, inter-annual variations of the seasonal pattern related to shifts in vegetation phenology due to different temperature and rainfall are usually not accounted for. We propose a transition to stochastic modelling and present a near real-time change detection method that combines a structural time series model with the Kalman filter. The model continuously adapts to new observations and allows to better separate phenology-related deviations from vegetation anomalies or land cover changes. The method is tested in a forest change detection application aiming at the assessment of damages caused by storm events and insect calamities. Forest changes are detected based on the cumulative sum control chart (CUSUM) which is used to decide if new observations deviate from model-based forecasts. The performance is evaluated in two test sites, one in Malawi (dry tropical forest) and one in Austria (temperate deciduous, coniferous and mixed forests) based on Sentinel-2 time series. Both forest areas are characterized by a distinct, but temporally varying leaf-off season. The presented change detection method shows overall accuracies above 99%, users’ accuracies of 76.8% to 88.6%, and producers’ accuracies of 68.2% to 80.4% for the forest change stratum (minimum mapping unit: 0.1 ha). Results are based on visually interpreted points derived by stratified random sampling. A further analysis revealed that increasing the time series density by merging data from two Sentinel-2 orbits yields better forest change detection accuracies in comparison to using data from one orbit only. The resulting increase in users’ accuracy amounts to 7.6%. The presented method is capable of near real-time processing and could be used for a variety of automated forest monitoring applications.

On change detection in a Kalman filter based tracking problem

2014 ◽  
Vol 105 ◽  
pp. 268-276 ◽  
Author(s):  
B. Moussakhani ◽  
J.T. Flåm ◽  
T.A. Ramstad ◽  
I. Balasingham
Keyword(s):  
Kalman Filter ◽  
Change Detection ◽  
Tracking Problem

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

An attentional bias for LEGO® people using a change detection task: Are LEGO® people animate?

2016 ◽  
Vol 70 (3) ◽  
pp. 219-231 ◽  
Author(s):  
Mitchell R. P. LaPointe ◽  
Rachael Cullen ◽  
Bianca Baltaretu ◽  
Melissa Campos ◽  
Natalie Michalski ◽  
...  
Keyword(s):  
Change Detection ◽  
Attentional Bias ◽  
Detection Task ◽  
Change Detection Task
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