Pansharpening for Multiband Images With Adaptive Spectral–Intensity Modulation

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.

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.

Pansharpening is an important way of integrating spatial and spectral information in the field of remote sensing. This field uses the complementary and redundant information between multispectral (MS) images and panchromatic (PAN) images to obtain high spectral and high spatial resolution images. Various pansharpening methods have been introduced so far, each one attempting to provide a pansharpened image with the least distortion and maximum preservation of spectral and spatial information. Due to the importance of this issue, there should be methods and indices to evaluate the performance of different pansharpening algorithms and assess the quality of pansharpened images. In this paper, a segmentation-based method for assessing the quality of fused images is proposed. The advantage of this approach over pixel-based methods is that the pixel-based methods consider the fused images as a set of separate pixels while segmentation can take into account useful spatial information such as neighbourhoods, textures, etc. In the proposed method, by using k-means clustering algorithm, the reference and pansharpened images are segmented into areas with similar spectral and spatial features and the corresponding segments of the images are compared. This method is tested on three real data sets acquired by Pleiades, GeoEye-1, and QuickBird sensors. Experimental results demonstrate the effectiveness of the proposed method in evaluation of the quality of fused images.

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.

A new IHS and wavelet based pansharpening algorithm for high spatial resolution satellite imagery

IntroductionPansharpening is an important remote sensing task that aims to produce high-resolution multispectral (MS) images by combining low-resolution MS images with high-resolution panchromatic (PAN) images. Although deep learning-based pansharpening has shown impressive results, the majority of these models frequently struggle to balance spatial and spectral information, resulting in artifacts and a loss of detail in pansharpened images. Furthermore, these models may fail to properly integrate spatial and spectral information, leading to poor performance in complex scenarios. Additionally, these models face challenges such as gradient vanishing and overfitting.MethodsThis paper proposes a dual-path and multi-scale pansharpening network (DMPNet). It consists of three modules: the feature extraction module (FEM), the multi-scale adaptive attention fusion module (MSAAF), and the image reconstruction module (IRM). The FEM is designed with two paths, namely the primary and secondary paths. The primary path captures global spatial and spectral information using dilated convolutions, while the secondary path focuses on fine-grained details using shallow convolutions and attention-guided feature extraction. The MSAAF module adaptively combines spatial and spectral data across different scales, employing a self-calibrated attention (SCA) mechanism for dynamic weighting of local and global contexts and a spectral alignment network (SAN) to ensure spectral consistency. Finally, to achieve optimal spatial and spectral reconstruction, the IRM decomposes the fused features into low- and high-frequency components using discrete wavelet transform (DWT).ResultsThe proposed DMPNet outperforms competitive models in terms of ERGAS, SCC (WR), SCC (NR), PSNR, Q, QNR, and JQM by approximately 1.24%, 1.18%, 1.37%, 1.42%, 1.26%, 1.31%, and 1.23%, respectively.DiscussionExtensive experimental results and evaluations reveal that the DMPNet is more efficient and robust than competing pansharpening models.

Fusion of Remote Sensing Images Using Improved ICA Mergers Based on Wavelet Decomposition

Pansharpening is a process of combining a high-resolution Panchromatic (PAN) image with a low-resolution Multispectral (MS) image to obtain a high-resolution MS image. Pansharpening can not only overcome the physical and technical limitations of satellite sensors, but also improve the quality of images and obtain a more detailed description of the scene. In this paper, a novel method of Pansharpening is proposed which adopts the ARSIS concept. The spatial information missing in the low-resolution MS image can be extracted from the PAN image. This is achieved by image decomposition based on sparse representation. Then, the information is injected into the MS bands by the details injection model. In the dictionary training, we adapt Online Sparse Dictionary Learning (OSDL), which can shorten the dictionary training time and improve the fusion image effect. Experimental results show that the fusion image of the proposed method has stronger spectral performance and more detailed spatial information.

Fusion of hyperspectral and panchromatic images using structure tensor and matting model

Pansharpening technology plays a crucial role in remote sensing image processing by integrating low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images. This process addresses the limitations of satellite sensors, which cannot directly capture HRMS images. Despite significant developments achieved by deep learning-based pansharpening methods over traditional approaches, most existing techniques either fail to account for the modal differences between LRMS and PAN images, relying on direct concatenation, or use similar network structures to extract spectral and spatial information. Additionally, many methods neglect the extraction of common features between LRMS and PAN images and lack network architectures specifically designed to extract spectral features. To address these limitations, this study proposed a novel three-branch pansharpening network that leverages both spatial and frequency domain interactions, resulting in improved spectral and spatial fidelity in the fusion outputs. The proposed method was validated on three datasets, including IKONOS, WorldView-3 (WV3), and WorldView-4 (WV4). The results demonstrate that the proposed method surpasses several leading techniques, achieving superior performance in both visual quality and quantitative metrics.

Deep learning methods have gained rapid development in hyperspectral pansharpening (HP) due to powerful spatial–spectral feature extraction ability. However, most of these methods are optimized using a single reconstruction objective. It is difficult for these methods to find a balance between spectral preservation and spatial preservation. Furthermore, these methods adopt interpolation or convolution to upsample the hyperspectral images (HSIs), which tends to cause noticeable spectral distortion. To conquer these issues, a novel multiobjective guided divide-and-conquer network (MO-DCN) is proposed for HP. It consists of a deconvolution long short-term memories (LSTMs) network (DLSTM) and a divide-and-conquer network (DCN). DLSTM leverages bi-direction learning to upsample HSIs by considering 3-D spatiotemporal dependencies. Then, DCN designs a two-branch architecture to reconstruct spatial and spectral information from upsampled HSIs and panchromatic images (PANIs), respectively, where the spatial branch designs an attention-in-attention module (AIAM) to emphasize complementary attention in a coarse-to-fine way. Finally, co-improvement of spatial and spectral information is formulated as an Epsilon-constraint-based multiobjective optimization. The Epsilon constraint method transforms one objective into a constraint and regards it as a penalty bound to make an excellent tradeoff between different objectives. Experimental results demonstrated that the proposed method markedly improves pansharpening performance in both the spatial and spectral domains and has superior fusion performance than state-of-the-art methods.

As a branch of Internet of Things (IoT), network geographic information (WebGIS) has the task of enhancing remote sensing images, including correction, fusion, mapping, etc. Remote sensing images captured by different satellite platforms often present earth objects with various scales. Due to the different spatial resolutions of imaging sensors, the same object will appear in different sizes. Usually panchromatic (PAN) image, without spectral diversity, has high spatial resolution and multispectral (MS) image has lower spatial resolution but more spectral information. Fusing MS and PAN images, also known as pansharpening, will increase the spatial resolution of MS image without changing its spectral information. In the fusion process, inaccurate perception and extraction of the spatial details will lead to spectral and spatial distortions in the fused image. A new multi-scale structure perception based fusion method is proposed in this paper. Firstly, an improved structure-preserving filter is designed by using MS imagery as the reference to capture the structures in the PAN image. Then the structures having different scales are progressively perceived through a multi-scale decomposition scheme and the spatial details are accurately extracted from the PAN image. Finally, the details weighted by the band-dependent gains are injected into each band of MS images. Experimental results are compared with some recently proposed edge-preserving filtering based fusion methods. Visual analysis and objective evaluations demonstrate that the proposed method can produce high-quality fused output and achieve better performance than some state-of-the-arts.KeywordsFusionRemote sensingFilterMultispectral image

The fusion of traditional HSI mainly involves the fusion of HSI with MSI or PAN. Nevertheless, the combination of the former is constrained by the inadequate spatial resolution of MSI, and therefore the enhancement of the spatial resolution of HSI is inadequate. The latter is constrained by the substantial disparity in spectral information between PAN and HSI, leading to pronounced distortion of high-resolution HSI. To address this issue, this paper proposes IFNPMH, which is an integrated fusion network for used for the fusion of HSI, MSI, and PAN. The IFNPMH algorithm is capable of efficiently extracting spatial information from MSI and PAN, as well as spectral information from HSI. It combines the spatial and spectral information to produce fused images that possess precise spectral information and distinct spatial details. Specifically, IFNPMH incorporates the CBAM attention mechanism module to enhance feature representation capabilities. Finally, this article proposes a loss function that combines spectral and spatial information loss in IFNPMH training. The effectiveness of the proposed IFNPMH is demonstrated by extensive testing in comparison to other sophisticated approaches. This model has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.

Pan-sharpening - fusing the spatial and spectral information between panchromatic (PAN) and multispectral (MSI) or hyperspectral (HSI) imagery of a common scene is a hot topic in remote sensing due to a wide range of applications such as target detection, vegetation monitoring, and subsurface detection (e.g. landmines), among others. However, the focus of panchromatic sharpening is generally placed on visual quality of the resulting image and image-wide summary spectral accuracy metrics. Here we are interested in radiometrically accurate panchromatic sharpening of hyperspectral imagery with particular emphasis on spectral algorithm performance. Four pansharpening algorithms are applied to hyperspectral imagery and evaluated for spectral/radiometric fidelity. Two datasets from SHARE2012 were used: one which features rural scene elements and one which features an urban scene. Target detection was also performed to evaluate algorithm sharpening performance. We find that although visually the performance of the four algorithms were roughly similar, they differ in spectral/radiometric fidelity as well as performance in ACE target detection.

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.