Image change detection from difference image through deterministic simulated annealing

This paper proposes an automatic method based on the deterministic simulated annealing (DSA) approach for solving the image change detection problem between two images where one of them is the reference image. Each pixel in the reference image is considered as a node with a state value in a network of nodes. This state determines the magnitude of the change. The DSA optimization approach tries to achieve the most network stable configuration based on the minimization of an energy function. The DSA scheme allows the mapping of interpixel contextual dependencies which has been used favorably in some existing image change detection strategies. The main contribution of the DSA is exactly its ability for avoiding local minima during the optimization process thanks to the annealing scheme. Local minima have been detected when using some optimization strategies, such as Hopfield neural networks, in images with large amount of changes, greater than the 20%. The DSA performs better than other optimization strategies for images with a large amount of changes and obtain similar results for images where the changes are small. Hence, the DSA approach appears to be a general method for image change detection independently of the amount of changes. Its performance is compared against some recent image change detection methods

Techniques for automatic large scale change analysis of temporal multispectral imagery

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst\u27s job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change