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The Ehlers fusion method, which combines a standard intensity-hue-saturation (IHS) transform with fast Fourier transform filtering, is a high spectral characteristics preservation algorithm for multitemporal and multisensor data sets. However, for data sets of more than three bands, the fusion process is complicated, because only every three bands are fused repeatedly for multiple times until all bands are fused. The hyper-spherical colour sharpening (HCS) fusion method can fuse a data set with an arbitrary number of bands. The HCS approach uses a transform between an n-dimensional Cartesian space and an n-dimensional hyper-spherical space to get one single intensity component and n − 1 angles. Moreover, from a structural point of view, the hyper-spherical colour space is very similar to the IHS colour space. Hence, we propose to combine the Ehlers fusion with an HCS transform to fuse n-band data sets with high spectral information preservation, even hyper-spectral images. A WorldView-2 data set i...

Remote sensing technology is increasingly being used for rapid detection and visualization of changes caused by catastrophic events. This paper presents a semi-automated feature-based approach to the identification of building conditions especially in affected areas using geographic information systems (GIS) and remote sensing information. For image analysis, a new “detected part of contour” (DPC) feature is developed for the assessment of building integrity. The DPC calculates a part of the building contour that can be detected in the remotely sensed image. Additional texture features provide information about the area inside the buildings. The effectiveness of the proposed method is proved by high overall classification accuracy for two different study cases. The results demonstrate that the “map-to-image” strategy enables extracting valuable information from the remotely sensed image for each individual vector object, thereby being a better choice for change detection within urban areas.

The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a panchromatic image of high spatial resolution with multispectral image data of lower spatial resolution to obtain a high-resolution multispectral image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for image fusion to show their ability to fuse multitemporal and multi-sensor image data. A series of eight multitemp...

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1 : 10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1 : 10 to 1 : 30). However, even with a spatial resolution ratio as small as 1 : 30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1 : 30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

Image fusion is a technique that is used to combine the spatial structure of a high-resolution panchromatic image with the spectral information of a low- resolution multispectral image to produce a high-resolution multispectral image. Currently, image fusion techniques via color or statistical transforms such as the Intensity-Hue-Saturation (IHS) and principal component (PC) methods are still widely used. These methods create multispectral images of higher spatial resolution but usually at the cost of color distortions in the fused images. This is especially true if the wavelength range of the panchromatic image does not correspond to that of the employed multispectral bands or for multitemporal/multisensoral fusion. To overcome the color distortion problem, a number of new fusion methods have been developed over the last years. One of these is the Ehlers fusion algorithm, which is based on an IHS transform coupled with adaptive filtering in the Fourier domain. This method preserves the spectral character...

Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information.

Modelling urban objects and phenomena puts extremely high demands on the acquisition and analysis of the necessary data. In this context, digital airborne camera systems offer very high spatial resolutions and the simultaneous acquisition of elevation data—either through stereo‐matching or by laser scanning. However, with these data the user community faces new problems in their analysis. In response, region‐based classification approaches have been developed, but these are not yet fully automatic, reliable and transferable. Hence, this paper proposes a novel hybrid and multi‐scale methodology which performs a classification on all segmented levels (called ‘Classification on multiple Segment levels’, ComS). First investigations validate the applicability as well as the accuracy of the proposed methodology compared with conventional approaches, such as a supervised, pixel‐based maximum likelihood classification.

In this paper the potential of smartphone sensor data for verification of people trajectories derived from airborne remote sensing data are investigated and discussed based on simulated test recordings in the city of Osnabrueck, Germany. For this purpose, the airborne imagery is simulated by images taken from a high building with a typical single lens reflex camera. The smartphone data required for the analysis of the potential is simultaneously recorded by test persons on the ground. In a second step, the quality of the smartphone sensor data is evaluated regarding the integration into simulation and modelling approaches. In this context we studied the potential of the agent-based modelling technique concerning the verification of people trajectories.

In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m × 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial 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.

Since remote sensing provides more and more sensors and techniques to accumulate data on urban regions, three-dimensional representations of these complex environments gained much interest for various applications. In order to obtain three-dimensional representations, one of the most practical ways is to generate Digital Surface Models (DSMs) using very high resolution remotely sensed images from two or more viewing directions, or by using LIDAR sensors. Due to occlusions, matching errors and interpolation techniques these DSMs do not exhibit completely steep walls, and in order to obtain real three-dimensional urban models including objects like buildings from these DSMs, advanced methods are needed. A novel approach based on building shape detection, height estimation, and rooftop reconstruction is proposed to achieve realistic three-dimensional building representations. Our automatic approach consists of three main modules as; detection of complex building shapes, understanding rooftop type, and three-dimensional building model reconstruction based on detected shape and rooftop type. Besides the development of the methodology, the goal is to investigate the applicability and accuracy which can be accomplished in this context for different stereo sensor data. We use DSMs of Munich city which are obtained from different satellite (Cartosat-1, Ikonos, WorldView-2) and airborne sensors (3K camera, HRSC, and LIDAR). The paper later focuses on a quantitative comparisons of the outputs from the different multi-view sensors for a better understanding of qualities, capabilities and possibilities for applications. Results look very promising even for the DSMs derived from satellite data.

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.

Current and future remote sensing programs such as Landsat, SPOT, MOS, ERS, JERS, and the space platform's Earth Observing System (Eos) are based on a variety of imaging sensors that will provide timely and repetitive multisensor earth observation data on a global scale. Visible, infrared and microwave images of high spatial and spectral resolution will eventually be available for all parts of the earth. It is essential that efficient processing techniques be developed to cope with the large multisensor data volumes. This paper discusses data fusion techniques that have proved successful for synergistic merging of SPOT HRV, Landsat TM and SIR-B images. It is demonstrated that these techniques can be used to improve rectification accuracies, to depicit greater cartographic detail, and to enhance spatial resolution in multisensor image data sets.

Generally, image fusion methods are classified into three levels: pixel level (iconic), feature level (symbolic) and knowledge or decision level. In this paper we focus on iconic techniques for image fusion. There exist a number of established fusion techniques that can be used to merge high spatial resolution panchromatic and lower spatial resolution multispectral images that are simultaneously recorded by one sensor. This is done to create high resolution multispectral image datasets (pansharpening). In most cases, these techniques provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image. These techniques, when applied to multitemporal and/or multisensoral image data, still create spatially enhanced datasets but usually at the expense of the spectral consistency. In this study, a series of nine multitemporal multispectral remote sensing images (seven SPOT scenes and one FORMOSAT scene) is fused with one panchromatic Ikonos image. A number of techniques are employed to analyze the quality of the fusion process. The images are visually and quantitatively evaluated for spectral characteristics preservation and for spatial resolution improvement. Overall, the Ehlers fusion which was developed for spectral characteristics preservation for multi-date and multi-sensor fusion showed the best results. It could not only be proven that the Ehlers fusion is superior to all other tested algorithms but also the only one that guarantees an excellent color preservation for all dates and sensors.

Multi-directional decomposition and directional contrast based remote sensing images fusion algorithm is presented in this paper. Based on the properties of band contrast processing and contrast orientation of visual receptive fields, multi-directional contrast is introduced. The fusion procedure is as follows: first, Nonsubsampled Contourlet Transform of Panchromatic and multi-spectral images, second, construction of directional contrast sequences at different decomposition levels and different directions, third, construction of fused Nonsubsampled Contourlet Transform coefficients with multi-directional contrast fusion rule. Finally, a fused high spatial resolution multi-spectral image can be obtained with inverse Nonsubsampled Contourlet Transform. Experiment results show that multi-directional decomposition and directional contrast based remote sensing images fusion method can provide a very good fusion image. This method is better than ordinary wavelet transform based directional contrast fusion method. The fused multi-spectral image can provides more spatial information and preserve more spectral information.

In remote sensing, image fusion techniques are used to fuse high spatial resolution panchromatic and lower spatial resolution multispectral images that are simultaneously recorded by one sensor. This is done to create high resolution multispectral image datasets (pansharpening). In most cases, these techniques provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image. When applied to multitemporal and/or multisensoral image data, these techniques still create spatially enhanced datasets but usually at the expense of the spectral characteristics. In this study, eight multitemporal remote sensing images are fused with one panchromatic image to test eight different fusion techniques. The fused images are visually and quantitatively analyzed for spectral characteristics preservation and spatial enhancement. Of the employed methods, only the newly developed Ehlers fusion guarantees excellent color preservation and spatial improvement for all dates and sensors.

A contents correlation and Genetic Algorithm based remote sensing images fusion method is presented. Based on the imaging properties of Panchromatic images and multi-spectral images, contents correlation analysis concept is introduced. The fusion procedure is that Contourlet transform decomposition of Panchromatic and multi-spectral images, Analysis of redundancy and supplement relations of images contents, the construction of fusion method to redundancy components and supplement components, fusion algorithms optimization by using Genetic Algorithm. Finally, a fused image can be obtained with inverse Contourlet transform. Preliminary experiment results show that this method is better than ordinary wavelet transform based fusion method, IHS transform based fusion method and PCA transform based fusion method.

Visual perception based different scale remote sensing images fusion with multi-wavelet transform is discussed. Panchromatic image can provide detail geometric features, while multi-spectral image can provide very good spectral information. A high spatial resolution multi-spectral image can be obtained by combining these two images. A visual perception based remote sensing images fusion algorithm is presented in this paper. Experiment results show that visual perception based remote sensing images fusion algorithm with multi-wavelet transform is better than the ordinary wavelet transform based fusion method. The fused image can provides more spatial information. Keywords Visual perception, image fusion, multi-wavelet transform, multi-spectral image, Panchromatic image

While the increase in spatial resolution for digital images has been hailed as a significant progress, methods for their automated analyses (i.e. land cover mapping, change analysis, GIS integration) are still in the process of being developed. Object (or segment) based preprocessing techniques seem to be an adequate methodology because inter-class variances can be minimized and the image interpretation techniques of the human eye be mimicked. A number of papers has proven the validity of an segment based image analysis for automated processing, however, the question of appropriate data fusion techniques within this context has hardly been addressed. In this paper, we will investigate techniques for the combination of image fusion and segment based image analysis. The examples include (i) color preserving iconic fusion with subsequent segmentation and classification; (ii) 'cookie cutter' approach for the integration of high resolution RGB and low resolution hyperspectral image data for urban class material detection; and (iii) decision based integration of panchromatic high resolution data with multispectral images for the identification of settlement areas. We will show that the combination of segment based image analysis and fusion techniques at iconic, feature and decision level does indeed improve the final analysis and can be seen as a first step to a an automated result driven processing line. It has to be noted that there is no general theory for segment based image fusion although the feature level fusion seems to be the most promising path for a combination of the two processing paradigms.

Many applications of remote sensing - like, for example, urban monitoring - require high resolution image data for a correct determination of object geometry. The desired geometry of an object's surface is created in dieffernet studies by use of well known segmentation techniques. In this study, we evaluate the influence on image quality of analog and digital image data on the results of a image segmentation in eCognition. We compare the suitability of analog middle format camera data with image data produced by a commercial "of the shelf" digital camera taken during two campaigns in 2003 and 2004. Furthermore, the results of a multiresolution classification of an urban test site by use of both datasets will be presented. An outlook for future work on a multiresolution data fusion with hyperspectral data will be given at the end of this paper.

An adaptive multi-spectral and panchromatic remote sensing images fusion method is discussed. High spatial resolution panchromatic image can provide detail geometric features, while multi-spectral image can provide very good spectral information. A high spatial resolution multi-spectral image can be obtained by combining these two images. Multi-wavelet transform based fusion method is better than ordinary wavelet transform based fusion method, IHS fusion method and PCA fusion method. Different decomposition levels of multi-wavelet transform may lead to different images fusion performance. An adaptive multi-wavelet transform based fusion scheme is presented in this paper. Experiment results show that the adaptive multi-wavelet transform based fusion method can determine the best multi-wavelet decomposition level and provide the best fusion results.

This paper discusses the fusion of multi-spectral and panchromatic remote sensing images. A multi-wavelet transform based fusion method is presented after analyzing the basic principles of remote sensing imaging systems and the rational for image fusion. The simulation results show that the multi-wavelet transform based fusion method is superior to the ordinary wavelet transform based fusion method. The fused image provides more spatial and spectral information.

Multi-spectral and panchromatic remote sensing images fusion is discussed. High spatial resolution panchromatic image can provide detail geometric features ,while multi-spectral image can provide very good spectral information. A high spatial resolution multi-spectral image can be obtained by combining these two images. Multi-wavelets can be simultaneously orthogonal, symmetric, short supported and of high vanishing moments, all these properties can be used to maintain spatial information of panchromatic images and spectral information of multi-spectral images. A multi-wavelet transform based fusion scheme is presented in this paper. Experiment results show that the multi-wavelet transform based fusion method is better than the ordinary wavelet transform based fusion method and is superior to IHS fusion method and PCA fusion method. The fused image can provides more spatial information.

Data fusion methods are usually classified into three levels: pixel level (ikonic), feature level (symbolic) and knowledge or decision level. Here, we will focus on the development of ikonic techniques for image fusion. Image transforms such as the Intensity-Hue-Saturation (IHS) or Principal Component (PC) transform are widely used to fuse panchromatic images of high spatial resolution with multispectral images of lower resolution. These techniques create multispectral images of higher spatial resolution but usually at the cost that these transforms do not preserve the original color or spectral characteristics of the input image data. In this study, a new method for image fusion will be presented that is based on filtering in the Fourier domain. This method preserves the spectral characteristics of the lower resolution mul-tispectral images. Examples are presented for SPOT and Ikonos panchromatic images fused with Landsat TM and Iko-nos multispectral data. Comparison with existing fusion techniques such as IHS, PC or Brovey transform prove the su-periority of the new method. While in principle based on the IHS transform (which usually only works for three bands), the method is extended to any arbitrary number of spectral bands. Using this approach, this method can be applied to sharpen hyperspectral images without changing their spectral behavior.

The motivation for data fusion is to reduce the limitations and uncertainties associated with data coming from a single sensor only. In the context of remotely sensed data the fusion is often performed by combining high spatial with high spectral resolution imagery at different levels. In contrast to pixel-based approaches like the IHS-transformation, in this paper we will focus on a fusion of data at the feature level. In high spatial resolution data the geometry of urban objects can be determined very accurately. But high spatial resolution data often contains low spectral information such as, for example, a three band RGB image. Thus, similar feature values for thematic classes like water, dark pavements or dark rooftops lead to classification errors. If hyperspectral data is used to classify urban materials, the definition of endmembers representing those materials is needed. The problem is that endmembers representing urban surface types are often the result of a mixture of spectral pure materials which leads to flat spectra. Consequently, those thematic endmembers can hardly be detected by standard algorithms like the Pixel Purity Index (PPI) so that standard classification procedures fail. In order to improve the classification process, our approach fuses hyperspectral data recorded by the HyMap sensor with high spatial resolution imagery (digital orthophotos) for a combined endmember selection, classification, and structural analysis. The endmember for the thematic classes will be determined in a semi-automatic process. After a segmentation of the high spatial resolution dataset the resulting segments will be used to detect those pixels in the hyperspectral data sets, which represent candidates for the definition of thematic endmembers. The endmembers are stored in a spectral library and are used for the classification of hyperspectral data. The segments in the high spatial resolution data will be processed based upon the classification of the hyperspectral dataset and the application of overlay rules.

For a geographic analysis of multispectral scanner data from aircraft and their integration in spatial databases and geographic integration systems (GIS), geometric registration/rectification of the scanner imagery is required as a first step. Usually, one has to rely on global mapping functions such as polynomial equations as provided by most commercial image processing systems. These techniques have been proven to be very effective and accurate for satellite images. However, there are a umber of shortcomings when this method is applied to aircraft data. We see the multiquadric interpolation method as a promising alternative. The multiquadric function was first developed for the interpolation of irregular surfaces. It could be modified, however, to be used for image correction of remotely sensed data. In this form, it is particularly suited for the rectification of remote sensing images of large scale and locally varying geometric distortions. The multiquadric interpolation method yields a perfect fit at the used control points (CPs). With this, it is necessary to withhold independent test points that can be used for accuracy assessment. Within the registration/rectification process, all CPs contribute to the geometric warping of any given pixel in the image. Their effects, however, are weighted inversely to the distances between CPs and the current pixel location. The paper presents the multiquadric interpolation techniques and demonstrates successful application with airborne scanner data.