Daniel Tomowski

Combined Edge Segment Texture Analysis for the Detection of Damaged Buildings in Crisis Areas

This paper describes the results of a new combined method that consists of a cooperative approach of several different algorithms for automated change detection. These methods are based on isotropic frequency filtering, spectral and texture analysis, and segmentation. For the frequency analysis, different band pass filters are applied to identify the relevant frequency information for change detection. After transforming the multitemporal images using a fast Fourier transform and applying the most suitable band pass filter to extract changed structures, we apply an edge detection algorithm in the spatial domain. For the texture analysis, we calculate the parameters energy and homogeneity for the multitemporal datasets. Then a principal component analysis is applied to the new multispectral texture images and subtracted to get the texture change information. This method can be combined with spectral information and prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination of the change algorithms is applied to calculate the probability of change for a particular location. This Combined Edge Segment Texture (CEST) method was tested with high-resolution remote-sensing images of the crisis area in Darfur (Sudan). Our results were compared with several standard algorithms for automated change detection, such as image difference, image ratio, principal component analysis, multivariate alteration detection (MAD) and post classification change detection. CEST showed superior accuracy compared to standard methods.

On segment based image fusion

The new generation of satellite and aircraft sensors provides image data of very and ultra high resolution which challenge conventional aerial photography. The high-resolution information, however, is acquired only in a panchromatic mode whereas the multispectral images are of lower spatial resolution. The ratios between high resolution panchromatic and low resolution multispectral images vary between 1:2 and 1:8 (or even higher if different sensors are involved). Consequently, appropriate techniques have been developed to merge the high resolution panchromatic information into the multispectral datasets. These techniques are usually referred to as pansharpening or data fusion. The methods can be classified into three levels: pixel level (iconic) fusion, feature level (symbolic) fusion and decision level fusion. Much research has concentrated on the iconic fusion because there exists a wealth of theory behind it. With the advent of object or segment oriented image processing techniques, however, feature based and decision based fusion techniques are becoming more important despite the fact that these approaches are more application oriented and heuristic. Within this context, the integration of GIS based information can easily be accomplished. The features can come from a specific segmentation algorithm or from an existing GIS database. Within the context of feature and decision based fusion, we present two exemplary case studies to prove the potential of decision and feature based fusion. The examples include

Colour and texture based change detection for urban disaster analysis

A rapid visualisation of change in urban crisis areas is an important condition for planning and coordination of help. For automated change detection, a large number of algorithms has been proposed and developed. This paper describes the results of a colour and texture based change detection approach that was applied to satellite and aircraft images of the earthquake region in Haiti. In our integrated methodology, we calculate firstly new colour texture images which are based on the feature ‘energy’. This is performed for every channel in the visible colour spectrum at two different times after a radiometric harmonisation. Combined with a principal component analysis for each texture image and a subsequent histogram optimisation, we subtract the texture elements to visualise the occurred change. As result, it is not only possible to automatically delineate areas of change but also to distinguish between different types of change.

Detektion von zerstörten Gebäuden in Krisengebieten aus pan-chromatischen Daten

Das Ziel dieses Artikels ist die Analyse von Veranderungen in Gebieten, in denen sich Katastrophen mit plotzlichen Anderungen an Gebauden und der Infrastruktur ereignet haben. Standardverfahren der Veranderungsanalyse fuhren zu keinem zufriedenstellenden Ergebnis, daher wurde ein neues Verfahren entwickelt. Die in diesem Artikel dargestellte Methode erlaubt eine schnelle Detektion und Visualisierung von Veranderungen in Krisen- und Katastrophengebieten. Dies ist eine wichtige Voraussetzung fur die Planung und Koordination von Hilfskrafteinsatzen. Die vorgeschlagene Methode basiert auf Frequenzanalysen, Segmentierung und Texturmerkmalen. Sie kombiniert die unterschiedlichen Ansatze in einem Verfahren mittels eines Entscheidungsbaumes. Im Vergleich mit funf Standardverfahren zeigte dieser neue Ansatz die besten Resultate.