An Optimal Fusion Approach for Optical and SAR Images

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the synthetic aperture radar (SAR) interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter (MKF). Each study case presents also the results achieved by the proposed techniques applied to real data. Ó

In remote sensing applications, lower spatial resolution multispectral images are fused with higher spatial resolution panchromatic ones. The objective of this fusion process is to enhance the spatial resolution of the multispectral images to make important features more apparent for human or machine perception. This enhancement is performed by injecting the high frequency component of the panchromatic image into the lower resolution ones without deteriorating the spectral component in the fused product. In this work, we propose a novel pixel based image fusion technique which exploits the statistical properties of the input images to compose the outcome images. Criteria for an optimal image fusion are proposed. The fused image is essentially constructed by using the statistical properties of panchromatic and multispectral images within a window to determine the weighting factors of the input images. This paper describes the principles of the proposed approach, assesses its properties and compares it with other popular fusion techniques. This study is carried out using Ikonos, QuickBird and SPOT images over areas with both urban and rural features. Analytical derivation, numerical analysis and graphic results are presented to support our discussions.

Boletim de Ciências Geodésicas, 2012

Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutions and polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusion algorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused images were performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as "urban" and "agricultural". It has been observed that some of the methods have enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers' spectral quality is far better than those of the other methods. HPF performs almost best in agricultural areas for both SAR images.

2011

Image fusion is important algorithm in many remote sensing applications like visualization, identification of the boundary of the object, object segmentation, object analysis. Most widely used IHS method preserves the spatial information but distorts the spectral information during fusion process. IHS method is also limited to the three bands. In this paper, the MS based difference with respect to its mean image is used to reconstruct the fused image. In this the mean of the MS band is replaced by the mean of the PAN image. In this way both spatial and spectral information are taken into account in the process of image fusion. We have considered actual dataset of IKONOS four band image for our experiment. It has been observed from the simulation results that the proposed algorithm preserves both spatial and spectral information better than compared standard algorithm and it also significantly improves the universal quality index which measures the visual quality of fused image.

Transferring spatial details of a high-resolution image into a low-resolution one is called image fusion. There are some different fusion methods introduced. Due to the nature of fusion process, these methods may damage the spectral quality of the low-resolution multispectral image to a certain extent. In the literature, there are some metrics that are used to evaluate the quality of the fused images. Depending on their mathematical algorithms, these quality metrics may result in misleading results in terms of spectral quality in the fused images. If fusion process is successful, the classification result of the fused image should not be worse than the result acquired from raw multispectral image. In this study, Worldview-2, Landsat ETM+ and Ikonos multispectral images are fused with their own panchromatic bands and another Ikonos image is fused with Quickbird pan-sharpened image using IHS, CN, HPF, PCA, Multiplicative, Ehlers, Brovey, Wavelet, Gram-Schmidt and Criteria Based fusion...

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013

The task of enhancing the perception of a scene by combining information captured from different image sensors is usually known as multisensor image fusion. This paper presents an area-based image fusion algorithm to merge SAR (Synthetic Aperture Radar) and optical images. The co-registration of the two images is first conducted using the proposed registration method prior to image fusion. Segmentation into active and inactive areas is then performed on the SAR texture image for selective injection of the SAR image into the panchromatic (PAN) image. An integrated image based on these two images is generated by the novel area-based fusion scheme, which imposes different fusion rules for each segmented area. Finally, this image is fused into a multispectral (MS) image through the hybrid pansharpening method proposed in previous research. Experimental results demonstrate that the proposed method shows better performance than other fusion algorithms and has the potential to be applied to the multisensor fusion of SAR and optical images.

2014

Most Earth observational satellites are not able to acquire high spatial and spectral resolution data simultaneously because of design or observational constraints. To overcome such limitations, image fusion techniques are use. Image fusion is process combine different satellite images on a pixel by pixel basis to produce fused images of higher value. The value adding is meant in terms of information extraction capability, reliability and increased accuracy. The objective of this paper is to describe basics of image fusion, various pixel level mage fusion techniques for evaluating and assessing the performance of these fusion algorithms. Keywords— -Image Fusion, Pixel Level, Multi-sensor, IHS, PCA, Multiplicative, Brovey, DCT, DWT. INTRODUCTION Image Fusion is process of combine two different images which are acquired by different sensor or single sensor. Output image contain more information than input images and more suitable for human visual perception or for machine perception. ...

The usefulness of remote sensing data, in particular of images from Earth observation satellites, largely depends on their spectral, spatial and temporal resolution. Each system has its specific characteristics providing different types of parameters on the observed objects. Focussing on operational and most commonly used commercial remote sensing satellite sensors, this paper describes how image fusion techniques can help increase the usefulness of the data acquired. There are plenty of possibilities of combining images from different satellite sensors. This paper concentrates on the existing techniques that preserve spectral characteristics, while increasing the spatial resolution. A very common example is the fusion of SPOT XS with PAN data to produce multispectral (3band) imagery with 10 m ground resolution. These techniques are also referred to as image sharpening techniques. A distinction has to be made between the pure visual enhancement (superimposition) and real interpolation of data to achieve higher resolution (e.g. wavelets). In total, the paper describes a number of fusion techniques, such as RGB colour composites, Intensity Hue Saturation (IHS) transformation, arithmetic combinations (e.g. Brovey transform), Principal Component Analysis, wavelets (e.g. ARSIS method) and Regression Variable Substitution (RVS), in terms of concepts, algorithms, processing, achievements and applications. It is mentioned in which way the results of various techniques are influenced by using different pre-processing steps as well as modifications of the involved parameters. All techniques are discussed and illustrated using examples of applications in the various fields that are part of ITC's educational programme and consulting projects.

International Journal of Engineering Sciences & Research Technology, 2014

Image fusion techniques have interest within the remote sensing community. The reason of this is that in most cases the new generation of remote sensors with very high spatial resolution acquires image datasets in two separate modes: the highest spatial resolution is obtained for panchromatic images (PAN) whereas multispectral information (MS) is associated with lower spatial resolution. Usually, the term ‘fusion’ gets several words to appear, such as merging, combination, synergy, integration and several others that express more or less the same meaning the concept have since it appeared in literature. Image fusion techniques can be classified into three categories depending on the stage at which fusion takes place; it is often divided into three levels, namely: pixel level, feature level and decision level of representation. This paper describes the concept of image fusion and its relevant methods.