Remote sensing images often contain many similar components, such as buildings, roads, and water surfaces, which have similar spectra and spatial structures. Although convolutional neural networks (CNNs) based on residual learning can provide excellent performance in pansharpening, the existing methods do not make full use of intrinsically similar information in images. Moreover, since the convolution operation is focused on the local region, even in a deep network, position-independent global information is difficult to obtain. In this article, an efficient nonlocal attention residual network (NLRNet) is proposed to capture the similar contextual dependencies of all pixels. Specifically, to reduce the difficulty of network training caused by the original nonlocal attention, we propose an efficient nonlocal attention (ENLA) mechanism and employ residual with zero initialization (ReZero) technology to make the signal easy to spread through the network. Furthermore, a spectral aggregation module (SpecAM) is proposed to generate fused images and adjust the corresponding spectral information. The experimental results for the QuickBird and WorldView3 data sets show that the proposed method is competitive with other advanced methods based on quality assessment and visual perception.

Remote sensing satellite images with a high spatial and temporal resolution play a crucial role in Earth science applications. However, due to technology and cost constraints, it is difficult for a single satellite to achieve both a high spatial resolution and high temporal resolution. The spatiotemporal fusion method is a cost-effective solution for generating a dense temporal data resolution with a high spatial resolution. In recent years, spatiotemporal image fusion based on deep learning has received wide attention. In this article, a spatiotemporal fusion method based on multiscale feature extraction and a spatial channel attention mechanism is proposed. Firstly, the method uses a multiscale mechanism to fully utilize the structural features in the images. Then a novel attention mechanism is used to capture both spatial and channel information; finally, the rich features and spatial and channel information are used to fuse the images. Experimental results obtained from two datasets show that the proposed method outperforms existing fusion methods in both subjective and objective evaluations.

Remote sensing spatiotemporal image fusion is a promising approach to acquire remote sensing data with high spatial and temporal resolution. While most deep neural network-based models have demonstrated high accuracy, they heavily depend on temporal information from the dataset, necessitating a pair of coarse and fine resolution image sets ( C ₁ - F ₁ ) taken near the prediction time. This reliance on temporal data makes these models challenging in practical applications. To tackle this challenge, this study introduces a time-insensitive spatiotemporal fusion model. An adaptive spatial distribution transformation module (ASDT) is proposed to enhance the spatiotemporal consistency of fine-resolution images by incorporating the spatial structure of coarse-resolution images. This module aims to improve the model’s performance in tasks less sensitive to time. Furthermore, an encoding-decoding structure is devised to address significant resolution variations in remote sensing images. The encoder uses a multi-level feature extraction module (MLFE) to capture features at multiple levels, reducing information loss and enhancing feature utilization. The decoder includes a cross-scale attention feature fusion and reconstruction module (CSAFFIR) to integrate features of different scales and semantic levels, thereby improving the overall performance of the fused image. Experimental findings from datasets collected by two satellite types demonstrate that the proposed HPLTS-GAN model surpasses existing two-input models in subjective and objective evaluations. Additionally, our approach shows competitiveness with current three-input models.

In the remote sensing image fusion field, fusion methods based on deep learning (DL) are the latest techniques in panchromatic sharpening (pansharpening). However, existing pansharpening methods based on neural networks cannot adequately inject the spatial feature information of panchromatic (PAN) images into fusion images, and they do not exploit the feature relationships between spatial locations, such as rows and columns of feature maps. To solve these problems, a multidimensional channel attention residual neural network (MCANet) is proposed in this article. To preserve the structural information in PAN images, a two-stream detail injection (TSDI) module is proposed, and the local skip connection operation is adopted to mine more spectral and structural information. A multidimensional channel attention (MCA) module is also designed to enable the network to learn the nonlinear mapping relationships between image spatial locations. In addition, a multiscale feature fusion (MSFF) module is designed to improve feature representation in the image fusion process, which is conducive to improving the pansharpening effect. The experimental results on the WorldView-2 (WV-2), GaoFen-2 (GF-2), and QuickBird (QB) datasets demonstrate that the proposed method outperforms state-of-the-art methods both visually and quantitatively.

Although deep neural networks (DNNs) have achieved great success in pansharpening, most of them lack transparency and interpretability. Currently, some DNNs methods utilize deep unfolding techniques to alleviate this problem. However, they do not consider the regularization term separately when solving the energy function that represents the image degradation process, making it difficult to extract complex prior information in the unfolding module. Therefore, this paper proposes a pansharpening method based on deep non-local unfolding. Specifically, we expand the iterative process of solving the energy function into the corresponding neural network modules, making each module have a certain physical meaning. Then, we decouple the prior operator containing the prior knowledge of the remote sensing image and approximate the solution using the network module. Meanwhile, we incorporate local and non-local self-similarity priors into the prior operator and design a two-branch prior module for learning the prior features and contribution weights adaptively. Finally, the fused image is corrected with the learned prior features to approximate the real image. Experimental results on datasets from two different types of satellites demonstrate the superiority of our approach.

In recent years, the powerful nonlinear modeling capability of convolutional neural networks (CNN) has led to an increasing number of researchers focusing on deep-learning-based pansharpening methods. However, due to the diversity of remote sensing image features and the limitations of the convolution operation, the existing methods are still inadequate in restoring the spatial details of complex remote sensing scenes. Therefore, in this article, we propose a simple and effective network for pansharpening methods. Specifically, in our hierarchical feature integration architecture, a multiscale grouping dilated block is designed to adequately capture fine-grained representations of multilevel scale features. At the same time, we propose a spatially self-attention block to adaptively improve the feature extraction process by establishing associations between features. The above blocks are connected in a hierarchical design, with selective reuse of features between layers and a good ability to explore new levels of features while reusing low-level features. Our experiments with the GaoFen-2 satellite dataset, WorldView-2 satellite dataset, and WorldView-3 satellite dataset show that our proposed method is highly competitive with the existing excellent methods in both objective indicator evaluation and subjective visual evaluation.