The present work presents and evaluates a method to automatically select training samples of medium resolution satellite images within a supervised object oriented classification procedure. The method first takes a pair of images of the same area acquired in different dates and segments them in homogeneous regions on both images. Then a change detection algorithm takes stable segments as traini...

The research reported in this paper uses wireless sensor networks to provide salient information about spatially distributed dynamic fields, such as regional variations in temperature or concentration of a toxic gas. The focus is on deriving qualitative descriptions of salient changes to areas of high-activity that occur during the temporal evolution of the field. The changes reported include r...

A key issue to address in biological data integration is how to detect changes to the underlying biological data sources. In this demonstration, we present a novel system called BIDEL for detecting changes to genomic and proteomic data (sequences and annotations). We transform heterogeneous biological data to XML format (if necessary) and then detect changes between two versions of unordered XM...

In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to a change detection problem in EEG signals.

With the advent of remote sensing satellites, a huge repository of remotely sensed images is available. Change detection in remotely sensed images has been an active research area as it helps us understand the transitions that are taking place on the Earth’s surface . This paper discusses the methods and their classifications proposed by various researchers for change detection. Since use of so...

Analysis of the multi–spectral remotely–sensed images of the areas destroyed by an earthquake is proved to be a helpful tool for destruction assessments. The performance of such methods is highly dependant on the preprocess that registers the two shots before and after an event. In this paper, we propose a new fast and reliable change detection method for remotely– sensed images and analyze its...

Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematical...

A hybrid (signal-symbol) approach for detecting significant changes in imagery uses a signal-based change detection algorithm followed by a symbol-based change interpreter. The change detection algorithm is based on a linear prediction model which uses small patches from a reference image to locally model the corresponding areas in a newly acquired image, and vice versa. Areas that cannot be ac...

This paper presents PrBPRRC (Probabilistic Bipolar Radial Reach Correlation), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic backgro...

This work was supported, in part, by the Office of Naval Research & the SPAWAR System Center–San Diego & by the Rome Laboratory (grant F30602-01-2-05430), and by NSF (grant IIS-0123730). ABSTRACT Data on the web is constantly increasing. Many a times, users are interested in specific changes to the data on the web. Currently, in order to detect changes of interest, users have to poll the pages ...