Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics of these images. Traditional methods often prioritize spatial information while underutilizing the rich spectral context, leading to limited feature discrimination capabilities. To address these issues, we propose a novel frequency attention-enhanced network (FAENet), which incorporates a frequency attention model (FreqA) to jointly model spectral and spatial contexts. FreqA leverages discrete wavelet transformation (DWT) to decompose input images into distinct frequency components, followed by a two-stage attention mechanism comprising inner-component channel attention (ICCA) and cross-component channel attention (CCCA). These mechanisms enhance spectral representation, which is further refined through a self-attention (SA) module to capture long-range dependencies before transforming back into the spatial domain. FAENet’s encoder–decoder architecture facilitates multiscale feature refinement, enabling effective segmentation. Extensive experiments on the ISPRS Potsdam and LoveDA benchmarks demonstrate that FAENet outperforms state-of-the-art models, achieving superior segmentation accuracy. Ablation studies further validate the contributions of ICCA and CCCA. Moreover, efficiency comparisons confirm the superiority of the proposed FAENet over other models.

Cloud removal is a vital preprocessing step in optical remote sensing images (RSIs), directly enhancing image quality and providing a high-quality data foundation for downstream tasks, such as water body extraction and land cover classification. Existing methods attempt to combine spatial and frequency features for cloud removal, but they rely on shallow feature concatenation or simplistic addition operations, which fail to establish effective cross-domain synergistic mechanisms. These approaches lead to edge blurring and noticeable color distortions. To address this issue, we propose a spatial–frequency collaborative enhancement Transformer network named SFCRFormer, which significantly improves cloud removal performance. The core of SFCRFormer is the spatial–frequency combined Transformer (SFCT) block, which implements cross-domain feature reinforcement through a dual-branch spatial attention (DBSA) module and frequency self-attention (FreSA) module to effectively capture global context information. The DBSA module enhances the representation of spatial features by decoupling spatial-channel dependencies via parallelized feature refinement paths, surpassing the performance of traditional single-branch attention mechanisms in maintaining the overall structure of the image. FreSA leverages fast Fourier transform to convert features into the frequency domain, using frequency differences between object and cloud regions to achieve precise cloud detection and fine-grained removal. In order to further enhance the features extracted by DBSA and FreSA, we design the dual-domain feed-forward network (DDFFN), which effectively improves the detail fidelity of the restored image by multi-scale convolution for local refinement and frequency transformation for global structural optimization. A composite loss function, incorporating Charbonnier loss and Structural Similarity Index (SSIM) loss, is employed to optimize model training and balance pixel-level accuracy with structural fidelity. Experimental evaluations on the public datasets demonstrate that SFCRFormer outperforms state-of-the-art methods across various quantitative metrics, including PSNR and SSIM, while delivering superior visual results.

Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, which may inadvertently alter the intrinsic qualities of cloud-free areas, leading to image distortions. To address this issue, we propose a multi-stage frequency attention network (MFCRNet), a progressive paradigm for optical RSI cloud removal. MFCRNet hierarchically deploys frequency cloud removal modules (FCRMs) to refine the cloud edges while preserving the original characteristics of the non-cloud regions in the frequency domain. Specifically, the FCRM begins with a frequency attention block (FAB) that transforms the features into the frequency domain, enhancing the differentiation between cloud-covered and cloud-free regions. Moreover, a non-local attention block (NAB) is employed to augment and disseminate contextual information effectively. Furthermore, we introduce a collaborative loss function that amalgamates semantic, boundary, and frequency-domain information. The experimental results on the RICE1, RICE2, and T-Cloud datasets demonstrate that MFCRNet surpasses the contemporary models, achieving superior performance in terms of mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), validating its efficacy regarding the cloud removal from optical RSIs.

Optical remote sensing images are frequently contaminated by clouds and shadows, leading to information loss that significantly degrades the performance of Earth observation tasks. Synthetic aperture radar (SAR), with its cloud-penetrating capability, serves as a powerful complement to optical imagery under dense cloud coverage. However, most existing SAR–optical fusion approaches mainly rely on spatial-domain operations, underutilizing frequency-domain structural cues and lacking effective strategies to resolve spatial misalignment between heterogeneous modalities. To address the limitations above-mentioned, we propose FCFNet, a frequency-domain-guided cross-modal feature fusion network that jointly exploits spatial and frequency representations for cloud removal. Specifically, the FCFNet comprises three key components: a deformable gated fusion (DGF) module to adaptively align SAR and optical features via learnable spatial offsets and channel-wise modulation; a frequency-aware fusion (FAF) module that decomposes features into distinct frequency bands for selective integration of structural and textural information; and a frequency-domain attention (FDA) mechanism that enhances high-frequency detail recovery in decoding. Additionally, we formulate a jointly optimized loss function that aligns with the network’s dual-domain design, promoting accurate reconstruction through spatial supervision and frequency-based constraints. Extensive experiments on the SEN12MS-CR dataset demonstrate that FCFNet outperforms state-of-the-art methods across various quantitative metrics, particularly under heavy cloud occlusion scenarios, validating the effectiveness of our frequency-space cooperative modeling strategy.

Hyperspectral pansharpening is an efficient approach to obtaining high-resolution hyperspectral images (HR-HSIs) by fusing low-resolution hyperspectral images (LR-HSIs) with high-resolution panchromatic images (HR-PANs). However, the spatial and spectral distortions in reconstructed HR-HSIs are almost inevitable due to the modal gap between LR-HSIs and HR-PANs. Therefore, the performance of multi-source features fusion largely hinges on the ability to extract and align heterogeneous features across modalities. Most of the existing methods focus on integrating decoupled spatial and spectral information from different sources directly, which poses a dual challenge in aligning both spatial and spectral features effectively. To address the issues above-mentioned, a novel method named Interactive-Guided Fusion Network (IGFNet) is proposed, which is built upon a multi-stage progressive fusion framework. A high-resolution branch (HR) is introduced to interactively guide the alignment between cross-modal features, by fusing up-sampled HSI and PAN as a joint spatial-spectral guidance signal. Furthermore, the alignment is progressively conducted across stages, narrowing the modality gap and enhancing the representation of HR spatial-spectral feature. Additionally, we designed parameter-free spatial, spectral, and spatial-spectral attention mechanisms to extract global and local features effectively. Extensive experiments on reduced-resolution and full-resolution datasets demonstrate that IGFNet outperforms state-of-the-art across various metrics. Specifically, with a scaling factor of 4 on the Pavia University dataset, our method achieves a 2.09% relative improvement in PSNR, a 0.99% relative increase in SSIM, a 2.6% relative reduction in SAM, while reducing the parameter count by compared to the baseline MDA-Net.

Reconstructing high-resolution hyperspectral images (HR-HSIs) from low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) is an important multimodal remote sensing task for applications requiring both fine spatial details and reliable spectral characterization. However, existing Transformer-based HSI–MSI fusion methods still face difficulty in jointly preserving geometric structures and spectral continuity. Specifically, flattening two-dimensional image structures into one-dimensional token sequences tends to weaken local spatial connectivity, while standard self-attention does not explicitly model inter-band dependency, which may lead to structural distortion and spectral inconsistency in the fused results. To address these issues, this paper proposes an Adaptive Polyline Path Masked Attention Network (AdaPPMA-Net) for HSI-MSI fusion. First, an Adaptive Polyline Path Masked Attention mechanism is developed to explicitly encode horizontal and vertical geometric continuity, while a gating strategy is introduced to adaptively regulate positional constraints and suppress redundant dependencies. Second, a spectral enhancement module is embedded into the Transformer block to strengthen inter-band dependency modeling and alleviate the loss of spectral continuity during token interaction. Third, a spatial–spectral refinement (SSRefine) module is designed to recalibrate fused spatial–spectral representations, thereby improving reconstruction quality in the decoding stage. Extensive experiments on four public datasets demonstrate that AdaPPMA-Net consistently outperforms several state-of-the-art methods across multiple quantitative metrics. In particular, on the Washington DC Mall dataset, the proposed method raises PSNR by 4.2975 dB and lowers RMSE, ERGAS, and SAM by 39.03%, 39.35%, and 38.02%, respectively, compared with the strongest competing method. These results indicate that AdaPPMA-Net provides a more effective solution for high-fidelity multimodal remote sensing fusion.