ON THE CATEGORIZATION OF HIGH ACTIVITY OBJECTS USING DIFFERENTIAL ATTRIBUTE PROFILES

Change detection represents a broad field of research being on demand for different applications (e.g. disaster management and land use / land cover monitoring). Since the detection itself only delivers information about location and date of the change event, it is limited against approaches dealing with the category, type, or class of the change objects. In contrast to classification, categorization denotes a feature-based clustering of entities (here: change objects) without using any class catalogue information. Therefore, the extraction of suitable features has to be performed leading to a clear distinction of the resulting clusters.In previous work, a change analysis workflow has been accomplished, which comprises both the detection, the categorization, and the classification of so-called high activity change objects extracted from a TerraSAR-X time series dataset. With focus on the features used in this study, the morphological differential attribute profiles (DAPs) turned out to be very promising. It was shown, that the DAP were essential for the construction of the principal components.In this paper, this circumstance is considered. Moreover, a change categorization based only on different and complementary DAP features is performed. An assessment concerning the best suitable features is given.</p

Generalization of the CoVAmCoh analysis for the interpretation of arbitrary insar images

Very high resolution InSAR image pairs have a tremendous content of information compared to a single image. To improve the visualization and the interpretation of InSAR image pairs RGB false color image products are very helpful e.g. the well-known ILU-image (Interferometric Land Use image). In this paper, the CoVAmCoh method which was already introduced in former studies is analyzed with the aim of obtaining a general rule set for visualization independent e.g. of sensor parameters (incidence angle, resolution etc.). CoVAmCoh™ stands for the RGB arrangement of the three layers Coefficient of Variation (CoV), mean intensity (Am2) and the coherence (Coh) of an interferometric SAR image pair. It has the potential for fast extraction of physical scatter characteristics of the scene (e.g. detection of vegetation or areas of changes)