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.
ABSTRACT:In this paper, we will investigate techniques for the combination of image fusion and segment based image analysis. We describe a color preserving iconic fusion with subsequent segmentation and classification, a 'cookie cutter' approach for the integration of high resolution RGB and low resolution hyperspectral image data and a decision based fusion technique, that combine high resolution panchromatic data with multispectral images. We will show that the combination of segment based image analysis and fusion techniques at iconic, feature and decision level can indeed improve the final analysis and can be seen as a first step to an automated processing line.
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