This paper presents an automatic context-sensitive technique robust to registration noise (RN) for change detection (CD) in multitemporal very high geometrical resolution (VHR) remote-sensing images. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis in an adaptive...

Remote sensing has been a vastly under-utilized resource involving a multi million dollar investment. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in real time and at desired confidence levels. Consider an imminent scenario: What message does a remote sensing-derived landcover landuse...

Composited wide field of view satellite data are used for many applications and increasingly for studies of global change. Although several compositing schemes have been suggested, all assume perfect geometric registration, which is not operationally feasible. In this study, models of the satellite imaging, geometric registration, and compositing processes are used to investigate the impact of ...

Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signa...

Abstract: We propose new tests to detect a change in the mean of a time series. Like many existing tests, the new ones are based on the CUSUM process. Existing CUSUM tests require an estimator of a scale parameter to make them asymptotically distribution free under the no change null hypothesis. Even if the observations are independent, the estimation of the scale parameter is not simple since ...

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We measure the ability of human observers to predict the next datum in a sequence that is generated by a simple statistical process undergoing change at random points in time. Accurate performance in this task requires the identification of changepoints. We assess individual differences between observers both empirically, and using two kinds of models: a Bayesian approach for change detection a...

Ever since the first digital computer was constructed and equipped with sufficient hardware to process large amounts of data, digital image processing has become an increasingly used and desirable technique in many different fields. This is why it is important to understand the theory behind it and master its applications. While the human eye often is a final judge for an image that is to be in...