S2WMamba: A Spectral-Spatial Wavelet Mamba for Pansharpening

• URL: http://arxiv.org/abs/2512.06330v1
• Date: Sat, 06 Dec 2025 07:15:12 GMT
• Title: S2WMamba: A Spectral-Spatial Wavelet Mamba for Pansharpening
• Abstract summary: Pansharpening fuses a high-resolution PAN image with a low-resolution multispectral (LRMS) image to produce an HRMS image.<n>Key difficulty is that jointly processing PAN and MS often entangles spatial detail with spectral fidelity.<n>We propose S2WMamba, which explicitly disentangles frequency information and then performs lightweight cross-modal interaction.
• Score: 28.735552538568154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pansharpening fuses a high-resolution PAN image with a low-resolution multispectral (LRMS) image to produce an HRMS image. A key difficulty is that jointly processing PAN and MS often entangles spatial detail with spectral fidelity. We propose S2WMamba, which explicitly disentangles frequency information and then performs lightweight cross-modal interaction. Concretely, a 2D Haar DWT is applied to PAN to localize spatial edges and textures, while a channel-wise 1D Haar DWT treats each pixel's spectrum as a 1D signal to separate low/high-frequency components and limit spectral distortion. The resulting Spectral branch injects wavelet-extracted spatial details into MS features, and the Spatial branch refines PAN features using spectra from the 1D pyramid; the two branches exchange information through Mamba-based cross-modulation that models long-range dependencies with linear complexity. A multi-scale dynamic gate (multiplicative + additive) then adaptively fuses branch outputs.On WV3, GF2, and QB, S2WMamba matches or surpasses recent strong baselines (FusionMamba, CANNet, U2Net, ARConv), improving PSNR by up to 0.23 dB and reaching HQNR 0.956 on full-resolution WV3. Ablations justify the choice of 2D/1D DWT placement, parallel dual branches, and the fusion gate. Our code is available at https://github.com/KagUYa66/S2WMamba.

Related papers

• Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion [25.15016853820625]
  Deep learning models have achieved excellent performance, but they often come with high computational complexity.<n>In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening.<n>A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet.
  arXiv  Detail & Related papers  (2025-09-27T14:10:51Z)
• Hyperspectral Mamba for Hyperspectral Object Tracking [56.365517163296936]
  A new hyperspectral object tracking network equipped with Mamba (HyMamba) is proposed.<n>It unifies spectral, cross-depth, and temporal modeling through state space modules (SSMs)<n>HyMamba achieves state-of-the-art performance on seven benchmark datasets.
  arXiv  Detail & Related papers  (2025-09-10T03:47:43Z)
• HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder [0.04332259966721321]
  Hyperspectral imagery provides rich spectral detail but poses unique challenges because of its high dimensionality in both spatial and spectral domains.<n>We propose textitHyperspectralMAE, a Transformer-based model for hyperspectral data that employs a textitdual masking strategy.<n>HyperspectralMAE achieves state-of-the-art transfer-learning accuracy on Indian Pines, confirming that masked dual-dimensional pre-training yields robust spectral-spatial representations.
  arXiv  Detail & Related papers  (2025-05-09T01:16:42Z)
• PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification [49.37555541088792]
  Phase-Amplitude Decoupling (PAD) is a frequency-aware framework that separates phase (modality-shared) and amplitude (modality-complementary) components.<n>This work establishes a new paradigm for physics-aware multi-modal fusion in remote sensing.
  arXiv  Detail & Related papers  (2025-04-27T07:21:42Z)
• A Hybrid Transformer-Mamba Network for Single Image Deraining [70.64069487982916]
  Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions. We introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
  arXiv  Detail & Related papers  (2024-08-31T10:03:19Z)
• Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution [49.902047563260496]
  We develop the first attempt to integrate the Vision State Space Model (Mamba) for remote sensing image (RSI) super-resolution. To achieve better SR reconstruction, building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed FMSR. Our FMSR features a multi-level fusion architecture equipped with the Frequency Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate Module (HGM)
  arXiv  Detail & Related papers  (2024-05-08T11:09:24Z)
• S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification [44.99672241508994]
  Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. We propose S$2$Mamba, a spatial-spectral state space model for hyperspectral image classification, to excavate spatial-spectral contextual features.
  arXiv  Detail & Related papers  (2024-04-28T15:12:56Z)
• SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening [14.293042131263924]
  We introduce a spatial-spectral integrated diffusion model for the remote sensing pansharpening task, called SSDiff. SSDiff considers the pansharpening process as the fusion process of spatial and spectral components from the perspective of subspace decomposition.
  arXiv  Detail & Related papers  (2024-04-17T16:30:56Z)
• FusionMamba: Efficient Remote Sensing Image Fusion with State Space Model [35.57157248152558]
  Current deep learning (DL) methods typically employ convolutional neural networks (CNNs) or Transformers for feature extraction and information integration. We propose FusionMamba, an innovative method for efficient remote sensing image fusion.
  arXiv  Detail & Related papers  (2024-04-11T17:29:56Z)
• HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening [60.89777029184023]
  Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. We present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively.
  arXiv  Detail & Related papers  (2022-03-04T18:59:08Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.