Fusion of United Sparse Principal Component Analysis Dictionary Based on Linear Unmixing Image Technique

In order to fuse two registered multi-spectral (MS) image and panchromatic (PAN) image in the same scene, a new remote sensing image fusion algorithm based on Principal Component Analysis (PCA) and Curvelet transform is proposed. The first principle component PC1 of MS image is extracted via PCA transform, at the same time, we perform the Morphology-Hat transform on the PAN image, and segment the transformed PAN image by the PCNN segmentation algorithm. Perform the Curvelet transform on the component PC1 of MS image and the PAN image after Morphology-Hat transform, and use different fusion rule to fuse different scale layers coefficients (coarse, detail and fine scale layer). For obtaining the fused image, we use the inverse Curvelet transform and inverse PCA transform to obtain the fused image. The experimental results illustrate that the proposed fusion algorithm outperforms Curvelet transform and other traditional fusion algorithms in whole such as intensity–hue–saturation, PCA, Brovey and Weighted Average both in visual effect and objective evaluation indexes (standard deviation, mean, information entropy, correlation coefficient, spectral distortions and deviation index).

This paper proposes an efficient and high-fidelity image fusion method based on adaptive smoothing filtering for panchromatic (PAN) and multispectral (MS) image fusion. The scale ratio reflects the ratio of spatial resolution between the panchromatic image and the multispectral image. When facing a multiscale fusion task, traditional methods are unable to simultaneously handle the problems of spectral resolution loss resulting from high scale ratios and the issue of reduced spatial resolution due to low scale ratios. To adapt to the fusion of panchromatic and multispectral satellite images of different scales, this paper improves the problem of the insufficient filtering of high-frequency information of remote sensing images of different scales by the classic filter-based intensity modulation (SFIM) model. It uses Gaussian convolution kernels instead of traditional mean convolution kernels and builds a Gaussian pyramid to adaptively construct convolution kernels of different scales to filter out high-frequency information of high-resolution images. It can adaptively process panchromatic multispectral images of different scales, iteratively filter the spatial information in panchromatic images, and ensure that the scale transformation is consistent with the definition of multispectral images. Using 15 common fusion methods, this paper compares the experimental results of ZY-3 with scale ratio 2.7 and SV-1 with scale ratio 4 data. The results show that the method proposed in this paper retains good spatial information for image fusion at different scales and has good spectral preservation.

In remote sensing, satellite images acquired from sensors provide either high spectral or high spatial resolution. The pansharpening framework is applied to remote-sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) images using information from panchromatic imagery. A multidecomposition pansharpening approach combining MS and panchromatic (PAN) images is proposed in this paper in order to bring the resolution of the low-resolution MS imagery up to that of the panchromatic images. In particular, multilevel wavelet decomposition is applied to the luminance-chrominance (YUV) space transformation (taking into account the red green and blue (RGB) bands) or extended-YUV transformation (taking into account the near infrared (NIR) band in addition to RGB) of the original MS channels, where geometrical details from the panchromatic image are introduced into the MS ones. Our approach contains a preprocessing step that consists of homogenizing the luminance, Y, and the panchromatic image reflectance, which are, respectively, a value integrated over a wavelength spectrum and simply a linear combination of some values in the same spectrum. Hence, as the panchromatic image reflectance and luminance reflectance correspond to different measurements, they do not correspond to the same physical information, which results in a difference between their histograms. Therefore, simple histogram matching is traditionally applied to panchromatic data to fit it to the luminance to avoid colour distortion after fusion. However, as the transformation concerns just the details of the panchromatic and MS images, a new scheme for matching the images which ignores the divergence between their approximations and maximizes the resemblance between their details is proposed in this work. After that, the fusion approach is applied, and in contrast to the original approach where the details of the fused MS luminance are set equal to the PAN luminance, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between the luminance and lowered PAN image is taken. Indeed, high-resolution geometrical details cannot be similar if the low-resolution details are not in good agreement. Besides, as the agreement between PAN and MS images depends on the occupation class, we have created a segmentation map and then computed separately the correlation in each region. Finally, the evaluation is done based on QuickBird and Pleiades-1A data sets showing rural and suburban areas. When compared to recent methods, our approach provides better results.

The intent of this study is to remotely monitor the status of revegetation growth in a reclaimed, previously mined region on the island of Milos, Greece. Quickbird multispectral (spatial resolution 2.4m×2.4m) and panchromatic (spatial resolution 0.6m×0.6m) images have been fused to obtain an optimal combination of the initial spatial and spectral resolution. The Blue (450nm-520nm), Green (520nm-600nm) and Near Infrared (760nm-900nm) bands of the multispectral image have been used for vegetation monitoring. Different fusion methods, like the Principal Component Analysis, the Intensity-Hue-Saturation transform and the Wavelet The goal of this study is to remotely monitor the status of re-vegetation growth in a reclaimed, previously mined region on the island of Milos, Greece. Quickbird multispectral (spatial resolution 2.4m×2.4m) and panchromatic (spatial resolution 0.6m×0.6m) images have been fused to obtain an optimal combination of the initial spatial and spectral resolution. The Blue (450nm-520nm), Green (520nm-600nm) and Near Infrared (760nm-900nm) bands of the multispectral image have been used for vegetation monitoring. Different fusion methods, like the Principal Component Analysis, the Intensity-Hue-Saturation transform and the Wavelet Analysis have been applied to Quickbird images. Both statistical (correlation coefficient, accuracy measures, etc.) and subjective (i.e., visual) measures have been used to evaluate the produced fused images. The degree to which each of the fused images retains the spectral and spatial features of the initial images has been thus estimated. Based on statistical measures, it has been found that the Additive Wavelet Principal Component "A Trous" and the Additive Wavelet Intensity Mallat methods effectively preserve most of the spectral information of the original multispectral image. On the other hand, the "A Trous" and Intensity-Hue-Saturation fusion techniques retain most of the spatial information of the panchromatic image. Additionally, the IHS transform offers a compromise between the spectral and spatial content of the fused image. Since the spectral content in the NIR band is of primary importance for monitoring re-vegetation growth, the Additive Wavelet Mallat and the IHS transforms are the most suitable choices.

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

In this paper, we propose a fuzzy-based multi-spectral (MS) and panchromatic (PAN) image fusion approach which provides a tradeoff solution between spectral and spatial fidelity and is able to preserve more detail in terms of spectral and spatial information. First, we perform principal component analysis on the multi-spectral images and utilise the first principal component to extract matched low and high frequency coefficients. We then apply fuzzy-based image fusion rules to fuse the first principal component with the PAN image, followed by fusing the approximation coefficients. The proposed approach is tested on several satellite images and shown to provide a feasible and effective approach.

A new image fusion method based on hue-saturation-value (HSV) color space is proposed, the improved HSV fuses multi-spectral (MS) and panchromatic (PAN) images to improve spatial information and preserve spectral characteristics. The main advantage of the new fusion method is a simple and efficient way which can maximize the extraction of the spatial information and eliminate the disturbance of spectral information in PAN image. the difference of the low components information between PAN and the value component is filtered from the PAN image, and then value component are replaced by the new PAN image, the new HSV color space perform reverse transform to obtain a multispectral image with the high spatial resolution.SPOT-5 and QuickBird MS and PAN images were employed to execute the existing HSV and improved HSV fusion methods. Qualitative and quantitative analyses and classification accuracy assessment were conducted to evaluate the performance of the fusion methods. The results demonstrate that the improved HSV is better than traditional HSV methods. The new fusion method can achieve a wide range of balance between high spatial resolution retention and spectral characteristic preservation.

Superresolution land cover mapping (SRM) is a technique for generating land cover maps with a finer spatial resolution than the input image. In general, either the original multispectral (MS) images or the spectral unmixing results of the MS image are used as input for SRM models. Panchromatic (PAN) images are often captured together with MS images by many remote sensors and provide more spatial information due to their higher spatial resolution compared with the MS image. In this paper, a spatially adaptive spatial–spectral managed SRM model (SA_SSMSRM) that incorporates both MS and PAN images is proposed. SA_SSMSRM aims to better smooth homogeneous regions of objects (which represent a territory within which there is a uniformity in terms of land cover class) and preserve land cover class boundaries simultaneously by using the PAN image pixel photometric distance (i.e., gray-level distance or pixel value difference). Homogeneous regions in the PAN images are usually characterized by the photometric (pixel value) similarity, whereas class boundaries are usually characterized by photometric dissimilarity. The SA_SSMSRM smoothing parameter, which controls the contribution of the prior term (which encodes prior knowledge about land cover spatial patterns), is designed to be spatially adaptive, with its value decreasing if the photometric similarity of neighboring PAN image pixels decreases. SA_SSMSRM was examined on high-spatial-resolution QuickBird images, IKONOS images, and Advanced Land Observing Satellite (ALOS) images with both MS and PAN data. Results showed that the proposed SA_SSMSRM can generate more accurate superresolution maps than other SRM models.

A novel image fusion method using IKONOS satellite images In satellite remote sensing, spatial resolutions of multispectral images over a particular region can be enhanced using better spatial resolution panchromatic images for the same region by a process called image fusion, or more generally data fusion. A fusion method is considered successful, if the spatial detail of the panchromatic image is transferred into the multispectral image and the spectral content of the original multispectral image is preserved in the fused product. This research proposes a novel image fusion algorithm which takes aim at producing both spatially enhanced and spectrally appealing fused multispectral images. In the proposed method, first an intermediary image is created using original panchromatic and multispectral images. This intermediary image contains the high frequency content of the panchromatic source image such that it is the one closest to the given multispectral source image (upsampled) by a natural semi inner product defined. The final fused image is obtained by applying a function which performs convex linear combination of the intermediary image and the upsampled multispectral image. The function used depends on the local standard deviations of the source images.To test the performance of the method, the images from IKONOS sensor are fused using the Brovey, IHS, PCA, wavelet transform based methods, and the proposed method. Both visual and quantitative evaluation results indicate that the proposed method yields to both spectrally and spatially appealing results as the wavelet transform based method, and it gives a better performance when both spatial detail enhancement and spectral content preservation in the fused products are considered. It is also obvious that the method has a potential to get better results if a better fitting, more complex function is found.

Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fusion methods—component substitution (CS) and multi-resolution analysis (MRA), which have different advantages—mixed approaches are possible. This paper proposes a fusion algorithm that combines the advantages of generalized intensity–hue–saturation (GIHS) and non-subsampled shearlet transform (NSST) with principal component analysis (PCA) technology to extract more spatial information. Therefore, compared with the traditional algorithms, the algorithm in this paper uses PCA transformation to obtain spatial structure components from PAN and MS, which can effectively inject spatial information while maintaining spectral information with high fidelity. First, PCA is applied to each band of low-resolution multispectral (MS) images and panchromatic (PAN) images to obtain the first principal component and to calculate the intensity of MS. Then, the PAN image is fused with the first principal component using NSST, and the fused image is used to replace the original intensity component. Finally, a fused image is obtained using the GIHS algorithm. Using the urban, plants and water, farmland, and desert images from GeoEye-1, WorldView-4, GaoFen-7 (GF-7), and Gaofen Multi-Mode (GFDM) as experimental data, this fusion method was tested using the evaluation mode with references and the evaluation mode without references and was compared with five other classic fusion algorithms. The results showed that the algorithms in this paper had better fusion performances in both spectral preservation and spatial information incorporation.

The pansharpening is a combination of multispectral (MS) and panchromatic (PAN) images that produce a high-spatial-spectral-resolution MS images. In multiresolution analysis–based pansharpening schemes, some spatial and spectral distortions are found. It can be reduced by adding spatial detail images of the PAN image into MS images. In the convolution neural network– (CNN) based method, the lowpass filter image extracted by the CNN model when MS and PAN images are directly applied into the input. The feature values are very high and reduce the conversion efficiency. In the proposed scheme, bi-dimensional empirical mode decomposition is used to extract the spatial detail information of the PAN image to reduce the feature values of the input. This extracted PAN image information is applied to the CNN to produce the non-linear changes in the image pixels and transformed into the perfect spatial detail image. It identifies the spatial and spectral detail quantity for the proposed scheme and it also varies with the different datasets automatically of the same satellite images. Simulation results in the context of qualitative and quantitative analysis demonstrate the effectiveness of proposed scheme applied on datasets collected by different satellites.

Image fusion is one of the important techniques for image information enhancing. In order to utilize respective information from different remote sensing images, we propose a new image fusion method based on the Principal Component Analysis (PCA) and feature product of wavelet transform. Firstly, the multi-spectral image is transformed with PCA. Secondly, the histogram-matched panchromatic image and the first principal component are decomposed into wavelet coefficients respectively. Thirdly, the first principal component of the multi-spectral image and the panchromatic image are merged with feature product of wavelet based fusion method, and the former is replaced with the merged data. Finally, the new multi-spectral image is obtained by inverse PCA. Some evaluation parameters are suggested and applied to compare the new method with those of PCA method, the combined PCA and traditional wavelet method and the combined PCA and local deviation of wavelet method. Subjective visual effect and objective statistical results indicate that the performance of the new method is better than those methods. It not only preserves spectral information of the original multi-spectral image well, but also enhances spatial detail information greatly.

Image fusion plays an important role in improving high resolution remote sensing images, as many Earth observation satellites provide both high-resolution panchromatic and multispectral images. To date, many image fusion techniques have been developed. Existing traditional image fusion techniques such as the intensity-hue-saturation (IHS) transform, wavelet 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 Quick Bird. However, the available algorithms can hardly meet a satisfactory fusion requirement for high resolution remote sensing images. Among the existing fusion algorithms, the IHS technique is the most widely used one technique. But the color distortion of this technique is often obvious, especially when high resolution multispectral images are fused with its panchromatic images. This study presents a new fusion approach that integrates both IHS and histogram match techniques to reduce the color distortion of high resolution remote sensing fusion results. Different high resolution remote sensing images have been fused with this new approach. The result proves that the concept of the proposed improved IHS is promising, and it does significantly improve the fusion quality compared to conventional IHS transform fusion techniques.

There are various sensors invented to capture the images of earth surface which are used to analyze the earth surface. The images captured by different sensors have different features. To analyze the earth surface images captured at different spectrums are used. In urban area characteristic of urban object are determined by not only their spectra but also by their structure. Various sensors captures the image at various spectrums knows as multispectral image but is with low spatial resolution. Some sensors captures the high resolution image also known as panchromatic image. Therefore it becomes very necessary and useful to fuse panchromatic and multispectral image to detect different area from the image easily. The fused image is multispectral image with high spatial resolution. The various methods of image fusion studied in this paper. Comparison of them is done is done statistically and visually and it is found that fused image is of greater quality and clarity than the original one and shows the features of that area more accurately.