Remote sensing image fusion using the curvelet transform

Interband structure modeling for Pan-sharpening of very high-resolution multispectral images

Many intermodality models (IMMs) have been established to define how panchromatic (Pan) high-frequency details should be injected into the multispectral (MS) bands in pan-sharpening. In this paper, IMMs are categorized into two types, namely regression coefficient (RC) models and intensity modulation (IM) models. The RC is simply the ratio of the local covariance of x and y to the local variance of x, where x and y are the approximation of the Pan image at the MS scale and the MS image, respectively. To compare these two IMM types, an ideal IMM representing the proper amount of high-frequency details which should be injected is formulated. This ideal IMM is exactly the same formulation as the RC model except that x and y are high-resolution Pan and MS images, respectively. Therefore, compared to the ideal IMM, the RC model is influenced by the scale effect since the local variance and covariance change as functions of scale. The IM model, represented by the model in the high-pass filtering (HPM) method, may introduce improper injections which are affected by landscape complexity, spectral response function, atmospheric conditions, and offset values for converting radiance into digital number (DN). Using the information from the high-resolution Pan image, an improved HPM method is proposed for shadow regions, which are quite common in very high-resolution images. Experimental results using a WorldView-2 image covering an urban area and a QuickBird image covering a rural area confirmed our analytical conclusions on different IMMs and indicate that the improved HPM model performs better than the original HPM.

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote sensing. In this paper, we apply the support value transform (SVT) to Ikonos image fusion. The fused saliency features are represented by support values and extracted by SVT. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The fusing results on Ikonos MS + Pan data demonstrate that the proposed image fusion method is effective and efficient.

Image fusion based on the Fourier and wavelet transform methods retain rich multispectral details but less spatial details from source images. Wavelets perform well only at linear features but not at non linear discontinuities because they do not use the geometric properties of structures. Curvelet transforms overcome such difficulties in feature representation. In this paper, we define a novel fusion rule via high pass modulation using Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms (FDCT) domain. For experimental study of this method Indian Remote Sensing (IRS) Resourcesat-1 LISS IV satellite sensor image of spatial resolution of 5.8m is used as low resolution (LR) multispectral image and Cartosat-1 Panchromatic (Pan) of spatial resolution 2.5m is used as high resolution (HR) Pan image. This fusion rule generates HR multispectral image at 2.5m spatial resolution. This method is quantitatively compared with Wavelet, Principal component analysis (PCA), High pass filtering(HPF), Modified Intensity-Hue-Saturation (M.IHS) and Grams-Schmidth fusion methods. Proposed method spatially outperform the other methods and retains rich multispectral details.

In this paper, we propose a novel method to enhance the pan-sharpening result of low-resolution multispectral images (MS) and high-resolution panchromatic images (Pan) by minimizing the spectral distortion engendered by the fusion process. In fact, spectral distortion is the most significant problem in many pan-sharpening techniques, due to the non linearity between Pan and MS images. In this method, an improvement of the Pan image is performed in order to enhance the correlation between Pan and MS images before pan-sharpening process. The proposed method is applied as a preprocessing by fusing the intensity image derived from MS image with the original Pan to get an improved Pan image which could be more correlated with MS image. And later, the pan-sharpening is applied on both MS and the improved Pan using any pan-sharpening technique. Simulation results of proposed method are compared in four different techniques, such as: Generalized IHS, DWT, Brovey and HPF. It has been observed that simulation results of this method preserves more spectral information and gets better visual quality compared with earlier reported techniques using original Pan.KeywordsPan-sharpeningFusionPanchromaticMultispectralSpectral distortion

Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, Sun and satellite heights and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage of a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth’s surface from space, that is for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape on different dates highlight that the de-hazing of MS before fusion is beneficial to an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI).

Data fusion technique can produce fused images with high spatial resolution and abundant spectral information. A new image fusion algorithm based on two-dimension PCA and Curvelet transform will be proposed according to image process models specialities in this paper. First of all, we performed 2DPCA on the MS image to get the 1st principle component (PC1); then we applied Curvelet transform in Pan Image and PC1; lastly decomposition coefficients obtained was processed according to certain rules to get fused coefficients, and afterwards, we performed inverse Curvelet transform on them to acquire fused sub-images. Then we performed inverse 2DPCA transform on the other components and the fused sub-images to get fused images. Experiments will be carried out via application of multispectral and panchromatic images, and it turns out that this new algorithm can improve spatial resolution greatly while maintaining spectral information.

Many pan-sharpening techniques have been developed to synthesize a multispectral (MS) image at high resolution by fusing MS images and panchromatic (Pan) images. Most existing pan-sharpening methods can achieve results with high spatial resolution, but the spectral distortion in the fused results is still a problem that needs to be solved. In this paper, an adaptive linear model is proposed to reduce the spectral distortion by weakening the dependence on the correlation between Pan and MS. The difference between a Pan image and the combination of MS images is estimated by least square optimization, and embedded into the proposed model as a virtual band. According to the adaptive model, an iterative pan-sharpening algorithm is proposed based on the steepest descent method, in which the virtual band is used as a local adaptive constraint to the optimized solution. The proposed method is tested on datasets acquired by IKONOS, QuickBird, and Landsat 7 ETM+ and compared with the existing methods. The quality measures and the visual impressions show that the proposed method is an efficient approach to preserving spectral information and represents strong robustness against various scenes and sensors.

Sharpening of a multispectral (MS) image with a panchromatic (Pan) image is an important way to synthesize a high resolution MS (HRMS) image that cannot be directly acquired by satellite sensor. In this paper, we propose a novel pan-sharpening scheme via graph regularized sparse coding (GRSC). Compared to the existing pan-sharpening methods based on sparse coding, our work has the following novelties. First, the image patches extracted from all the MS bands are clustered into several subsets with the K-mean algorithm. The image patches in each subset share the same or very similar structures. Second, we take into account the sparse and local manifold structures of the image patches in each subset, and use the GRSC algorithm to precisely describe these characteristics. Experimental results with QuickBird and IKONOS datasets demonstrate that the proposed method can acquire high quality HRMS images that preserve better spatial and spectral information.

Improving the quality of pan-sharpened multispectral (MS) bands is the main aim of the recent research on pan-sharpening. In this article, we present a novel image fusion method based on combining the curvelet transform and independent component analysis (ICA). The idea is to map the MS bands onto a statistically independent domain to determine the intensity component, which contains the common information of the MS bands, and then to pan-sharpen it using curvelets and a modified adaptive fusion rule. The proposed method is evaluated by visual and statistical analyses and compared with the curvelet (CVT)-based method using a context-based decision model, the CVT-based method using the Dempster–Shafer evidence theory, the improved ICA method, and the combined adaptive principle component analysis (PCA)–Contourlet method. The experimental results using QuickBird and WorldView-2 data show that the proposed method effectively reduces the spectral distortion while injecting spatial details into the fused bands as much as possible.

A novel method for multispectral image pansharpening based on high dimensional model representation

A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery

The pansharpening algorithm often faces an imbalance between spatial sharpness and spectral preservation, resulting in spectral and intensity inhomogeneities in the fused image. In this paper, to overcome this problem, we present a robust pansharpening method for multiband images with adaptive spectral–intensity modulation. In this method, we propose an adaptive spectral modulation coefficient (ASMC) and an adaptive intensity modulation coefficient (AIMC) to modulate the spectral and spatial information in the fused image, respectively. Among these coefficients, the ASMC is constructed based on two aspects: first, the details extracted from the panchromatic (PAN) and multispectral (MS) images; and second, the spectral relationship between each MS band. The AIMC is calculated by assessing the correlation and standard deviation between the PAN image and each MS band. Finally, we propose a mathematically linear model to combine ASMC and AIMC to achieve the fused image. Various remote-sensing satellite images were used in the evaluations. Experimental results indicate that the proposed method achieves outstanding performance in balancing spatial and spectral information and outperforms several state-of-the-art fusion methods in terms of both full-reference and no-reference metrics, and on visual inspection.

ABSTRACTThere is no such thing as ‘the best image fusion method’ in terms of both spectral and spatial fidelity. This fact encourages the researchers to develop more advanced approaches in order to optimally transfer the spatial details without distorting the colour content. Component substitution (CS)-based image fusion methods have been proven to produce sharper images but suffer from colour distortion. The aim of this study was to modify the CS-based Gram-Schmidt (GS) fusion method with the aid of the Genetic Algorithm (GA) to further improve its colour preservation performance. The GA was used to estimate a weight for each multispectral (MS) band. The obtained band weights were used to generate a low-resolution panchromatic (PAN) band, which plays a significant role in the performance of the GS method. The performance of the proposed approach was compared not only against the conventional GS, but also against widely-used CS-based, multiresolution analysis (MRA)-based and colour-based (CB) image fusion methods. The results indicated that the proposed GA-based approach produced spectrally and spatially superior results compared to the other methods used.