A compressed sensing-based pan-sharpening using joint data fidelity and blind blurring kernel estimation

This paper proposes a pan-sharpening method based on multi-direction subbands and deep neural networks. First, by utilizing the multi-scale and multi-direction properties of the nonsubsampled contourlet transform (NSCT), panchromatic (PAN) image is decomposed into the low frequency subbands in different resolutions and the high frequency subbands in different directions. Pan-sharpening method aims to fuse the high frequency subband coefficients of PAN image and the low frequency subband coefficients of multispectral (MS) image. Second, in order to better extract the feature of the high frequency subbands in different directions of PAN image, the deep neural network (DNN) is trained using the image patches of high frequency subbands of PAN image. Third, in the fusion stage, we exploit NSCT on the principal component of resampled low resolution (LR) MS image. The high frequency subbands of output high resolution (HR) MS image is obtained by forward propagation of the trained DNN, which input is the high frequency subbands of LR MS image. Finally, a new subband set is obtained by fusing the reconstructed high frequency subband and the original low frequency subband of LR MS image. The HR MS image is produced by executing the inverse transform of NSCT and adaptive PCA (A-PCA) on the new subband set. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.

Pansharpening is to fuse high-resolution panchromatic (PAN) images with low-resolution multispectral (LR-MS) images to generate high-resolution multispectral (HR-MS) images. Most of the deep learning-based pansharpening methods did not consider the inconsistency of the PAN and LR-MS images and used simple concatenation to fuse the source images, which may cause spectral and spatial distortion in the fused results. To address this problem, a multi-scale spatial-spectral attention guided fusion network for pansharpening is proposed. First, the spatial features from the PAN image and spectral features from the LR-MS image are independently extracted to obtain the shallow features. Then, a spatial-spectral attention feature fusion module (SAFFM) is constructed to guide the reconstruction of spatial-spectral features by generating a guidance map to achieve the fusion of reconstructed features at different scales. In SAFFM, the guidance map is designed to ensure the spatial-spectral consistency of the reconstructed features. Finally, considering the difference between multiply scale features, a multi-level feature integration scheme is proposed to progressively achieve fusion of multi-scale features from different SAFFMs. Extensive experiments validate the effectiveness of the proposed network against other state-of-the-art (SOTA) pansharpening methods in both quantitative and qualitative assessments. The source code will be released at https://github.com/MELiMZ/ssaff.

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.

Remote sensing image fusion (or pan-sharpening) aims at generating high resolution multi-spectral (MS) image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral image. In this paper, a deep convolutional neural network with two-stream inputs respectively for PAN and MS images is proposed for remote sensing image pan-sharpening. Firstly the network extracts features from PAN and MS images, then it fuses them to form compact feature maps that can represent both spatial and spectral information of PAN and MS images, simultaneously. Finally, the desired high spatial resolution MS image is recovered from the fused features using an encoding-decoding scheme. Experiments on Quickbird satellite images demonstrate that the proposed method can fuse the PAN and MS image effectively.

Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) images for various practical tasks such as ground-object classification and change detection. Currently, Transformers have been widely utilized in the pansharpening field because of their capability to model global representations. Transformer-based pansharpening methods capture a global representation of local patch sequences in the spatial domain, but they do not aggregate each pixel in the image, which hinders their ability to capture the global context and dependencies. To solve this issue, a novel pansharpening network based on Fourier with dropout in global modeling (FDGM) is proposed, which can better model global representations and obtain higher-quality fusion images without increasing computational complexity. First, an efficient Fourier with dropout block (FDB) is designed by transforming the features from the spatial domain to the frequency domain to capture the global information from the panchromatic (PAN) and low-resolution multispectral (LRMS) images. This study represents a novel attempt to explore the generalization performance of dropout in the Fourier frequency domain. Furthermore, a residual spatial-frequency fusion module (RSFM) based on frequency channel attention is designed to enhance feature representation capability and effectively facilitate the fusion of the spatial and frequency features. Finally, the shallow features extracted from the source images are combined with the RSFM output to generate the final fusion results. The results of numerous experiments demonstrate that FDGM surpasses other state-of-the-art approaches in visual perception and quantitative evaluation.

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.

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What’s more, these models often overlook the exploitation of PAN images’ inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network’s feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance.

Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.

Pansharpening is a process of fusing a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to obtain a high-resolution multispectral (HRMS) image. Convolutional neural networks (CNNs) have been commonly utilized in this field because of their remarkable learning capabilities. However, their convolutional operators limit the long-range feature extraction ability of CNN. Meanwhile, the Transformer models have exhibited strong capabilities in modeling long-range representations, but there are shortcomings in modeling local-range feature dependencies. To this end, we propose a novel synergistic transformer and CNN for pansharpening (STCP). First, a parallel U-shaped feature extraction module (PUFEM) is constructed for extracting the features of the LRMS and PAN images, which improves the feature representation ability for the two source images. In the PUFEM, combining the different feature learning capabilities of the CNN and transformer, we design a long-short-range feature integration block (LSFIB) to extract the short-range features and long-range features at different scales in parallel. Then, a channel attention module (CAM)-based feature fusion module (CFFM) is constructed to integrate the features extracted by the PUFEM. Finally, the shallow features from the PAN image are reused to provide detailed features, which are integrated with the fused features from the CFFM to achieve the final pansharpened results. Numerous experiments show that our STCP outperforms some state-of-the-art approaches both subjectively and objectively.

Distributed compressed sensing-based pan-sharpening with hybrid dictionary

Pansharpening is a remote sensing image fusion technique that combines a high-resolution (HR) panchromatic (PAN) image with a low-resolution (LR) multispectral (MS) image to produce an HR MS image. The primary challenge in pansharpening lies in preserving the spatial details of the PAN image while maintaining the spectral integrity of the MS image. To address this, this article presents a generative adversarial network (GAN)-based approach to pansharpening. The GAN discriminator facilitated matching the generated image’s intensity to the HR PAN image and preserving the spectral characteristics of the LR MS image. The performance in generating images was evaluated using the peak signal-to-noise ratio (PSNR). For the experiment, original LR MS and HR PAN satellite images were partitioned into smaller patches, and the GAN model was validated using an 80:20 training-to-testing data ratio. The results illustrated that the super-resolution images generated by the SRGAN model achieved a PSNR of 31 dB. These results demonstrated the developed model’s ability to reconstruct the geometric, textural, and spectral information from the images.

Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.

Pansharpening aims to produce a higher resolution multi-spectral (HRMS) image by fusing the spectral information in lower resolution multispectral (LRMS) image and the spatial information in corresponding high resolution panchromatic (PAN) image. In this work, we propose a multi-scale densely deep learning based pansharpening method. Following an end-to-end learning architecture, the proposed deep neural network contains three modules: 1) a parallel multi-scale convolutional layer is used to extract multiscale features of PAN image; 2) a global identity branch structure is adopted to preserve spectral structures; and 3) a dense learning block is integrated to improve the spectral-spatial expressive power. Compared with other state-of-the-art methods, experimental results obtained with our proposed method achieve high pansharpening quality in visualization and quantification.

Pan-sharpening is a process of acquiring a high resolution multispectral (MS) image by combining a low resolution MS image with a corresponding high resolution panchromatic (PAN) image. In this paper, we propose a new variational pan-sharpening method based on three basic assumptions: 1) the gradient of PAN image could be a linear combination of those of the pan-sharpened image bands; 2) the upsampled low resolution MS image could be a degraded form of the pan-sharpened image; and 3) the gradient in the spectrum direction of pan-sharpened image should be approximated to those of the upsampled low resolution MS image. An energy functional, whose minimizer is related to the best pan-sharpened result, is built based on these assumptions. We discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. To verify the effectiveness of our method, we qualitatively and quantitatively compare it with some state-of-the-art schemes using QuickBird and IKONOS data. Particularly, we classify the existing quantitative measures into four categories and choose two representatives in each category for more reasonable quantitative evaluation. The results demonstrate the effectiveness and stability of our method in terms of the related evaluation benchmarks. Besides, the computation efficiency comparison with other variational methods also shows that our method is remarkable.

In this article, we present a new pansharpening method, a zero-reference generative adversarial network (ZeRGAN), which fuses low spatial resolution multispectral (LR MS) and high spatial resolution panchromatic (PAN) images. In the proposed method, zero-reference indicates that it does not require paired reduced-scale images or unpaired full-scale images for training. To obtain accurate fusion results, we establish an adversarial game between a set of multiscale generators and their corresponding discriminators. Through multiscale generators, the fused high spatial resolution MS (HR MS) images are progressively produced from LR MS and PAN images, while the discriminators aim to distinguish the differences of spatial information between the HR MS images and the PAN images. In other words, the HR MS images are generated from LR MS and PAN images after the optimization of ZeRGAN. Furthermore, we construct a nonreference loss function, including an adversarial loss, spatial and spectral reconstruction losses, a spatial enhancement loss, and an average constancy loss. Through the minimization of the total loss, the spatial details in the HR MS images can be enhanced efficiently. Extensive experiments are implemented on datasets acquired by different satellites. The results demonstrate that the effectiveness of the proposed method compared with the state-of-the-art methods. The source code is publicly available at https://github.com/RSMagneto/ZeRGAN.