In recent years, intelligent processing of satellite videos has emerged as a significant research focus within the field of remote sensing, driven by the growing demand for enhanced spatial resolution. This need has led to increased interest in satellite video super-resolution (SVSR) algorithms, which aim to improve the quality of satellite imagery. However, many existing SVSR methods tend to neglect the global dependencies among frames in satellite videos, resulting in an incomplete utilization of spatio-temporal feature information. To tackle this issue, we propose a novel non-local spatio-temporal bidirectional recurrent network specifically designed for SVSR applications. Our approach employs a gate-guided deformable alignment module that effectively enhances feature alignment and fusion using a dynamic gating mechanism. This allows the network to adaptively focus on relevant features during the reconstruction process. Furthermore, we introduce a non-local spatio-temporal fusion module that integrates both temporal and spatial relationships over long sequences of frames, ensuring a comprehensive extraction of feature information. Through extensive experiments, our proposed method demonstrates superior performance compared to state-of-the-art SVSR techniques in terms of reconstruction quality. Additionally, it demonstrates outstanding performance in downstream satellite video applications, showcasing its potential in satellite video processing tasks. The source code is publicly available at https://github.com/Yu-Wang-0801/NSBRNet.

As the degradation factors of remote sensing images become increasingly complex, it becomes challenging to infer the high-frequency details of remote sensing images compared to ordinary digital photographs. For super-resolution (SR) tasks, existing deep learning-based single remote sensing image SR methods tend to rely on texture information, leading to various limitations. To fill this gap, we propose a remote sensing image SR algorithm based on a multi-scale texture transfer network (MTTN). The proposed MTTN enhances the texture feature information of reconstructed images by adaptively transferring texture information according to the texture similarity of the reference image. The proposed method adopts a multi-scale texture-matching strategy, which promotes the transmission of multi-scale texture information of remote sensing images and obtains finer-texture information from more relevant semantic modules. Experimental results show that the proposed method outperforms state-of-the-art SR techniques on the Kaggle open-source remote sensing dataset from both quantitative and qualitative perspectives.

Change detection (CD) is the main task in the remote sensing field. Binary change detection (BCD), which only focuses on the region of change, cannot meet current needs. Semantic change detection (SCD) is pivotal for identifying regions of change in sequential remote sensing imagery, focusing on discerning “from-to” transitions in land cover. The emphasis on features within these regions of change is critical for SCD efficacy. Traditional methodologies, however, often overlook this aspect. In order to address this gap, we introduce a change-aware guided multi-task network (CGMNet). This innovative network integrates a change-aware mask branch, leveraging prior knowledge of regions of change to enhance land cover classification in dual temporal remote sensing images. This strategic focus allows for the more accurate identification of altered regions. Furthermore, to navigate the complexities of remote sensing environments, we develop a global and local attention mechanism (GLAM). This mechanism adeptly captures both overarching and fine-grained spatial details, facilitating more nuanced analysis. Our rigorous testing on two public datasets using state-of-the-art methods yielded impressive results. CGMNet achieved Overall Score metrics of 58.77% on the Landsat-SCD dataset and 37.06% on the SECOND dataset. These outcomes not only demonstrate the exceptional performance of the method but also signify its superiority over other comparative algorithms.

Pan-sharpening is a technique used to create high-resolution multispectral (HRMS) images by merging low-resolution multispectral (LRMS) images with corresponding high-resolution panchromatic (PAN) images. Despite achieving state-of-the-art performance, existing panchromatic sharpening networks based on deep-learning (DL) methods suffer from spectral distortion and insufficient spatial texture enhancement. To address this challenge, this letter introduces a novel cross-spectral–spatial fusion network (CSSFN) for pan-sharpening remote sensing images. The network utilizes a cross-spectral–spatial attention block (CSSAB) to extract both the spectral information of the MS branch and the spatial information of the PAN branch. The spectral and spatial feature representations of remote sensing images are then progressively enhanced, which improves the fusion process and generates multispectral images with high spatial resolution. Our network outperforms other pan-sharpening methods on two publicly available datasets, as demonstrated by extensive experiments, yielding state-of-the-art results.

Pan-sharpening aims to fuse a high-spatial-resolution panchromatic (PAN) image with a low-spatial-resolution multispectral (MS) image to generate a high-quality, high-spatial-resolution MS image. Despite the development of existing pan-sharpening methods, they commonly face a critical challenge: insufficient modeling of deep-guided fusion mechanisms between spectral and spatial information, which hinders precise synergy and complementarity. To address this pivotal issue, this paper proposes a Spatial-Spectral Synergistic Mamba Network (S$^{3}$Mamba). S$^{3}$Mamba adopts a dual-branch guided structure, designed to balance spectral consistency and spatial detail preservation effectively. Specifically, the network introduces several key innovations: in the initial feature extraction stage, dedicated spectral Mamba modules and spatial Mamba modules are designed to leverage the Mamba architecture for deeply modeling inter-channel dependencies within the spectral dimension and for effectively capturing long-range spatial contextual information by applying Mamba along the spatial dimensions, respectively. Subsequently, during the feature fusion stage, we introduce a synergistic Mamba fusion module that processes features from different modalities using independent Mamba encoders and integrates a novel cross-modal feature guidance mechanism to achieve deep interconnection between spectral and spatial information. Furthermore, the network incorporates multimodal deep Mamba fusion modules that employ an early fusion strategy, ensuring that the fused features accurately represent both spectral and spatial information while also capturing multi-scale details through stacked fusion blocks and local convolutional refinement. Extensive quantitative and qualitative experimental results on three different datasets demonstrate that the proposed S$^{3}$Mamba method significantly outperforms existing state-of-the-art pan-sharpening techniques in terms of both objective evaluation metrics and visual perception quality.