FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network

• URL: http://arxiv.org/abs/2605.02137v1
• Date: Mon, 04 May 2026 01:37:25 GMT
• Title: FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network
• Abstract summary: FLoRA is a cross-modal framework that jointly reconstructs high-grained optical imagery and segments flood water regions from Sentinel 1 SAR.<n>Design enables multi-task learning across two complementary objectives: (a) SAR-to-optical translation for fine-grained RGB reconstruction and (b) flood water region segmentation for hydrologic interpretation.
• Score: 0.3186130813218338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate flood water mapping is critical for disaster management, yet current methods struggle to fully exploit the potential of spaceborne imagery. Optical data offers high interpretability but is limited by environmental conditions, whereas SAR provides reliable all-weather coverage with reduced visual interpretability. FLoRA (Fusion Latent for Optical Reconstruction and Area Segmentation) is a cross-modal multi-task framework that jointly reconstructs high-fidelity optical imagery and segments flood water regions from Sentinel 1 SAR by fusing the complementary strengths of optical and SAR data. During training, a lightweight optical teacher (driven by RGB and NDVI priors) provides pyramidal features that guide SAR representations into a fusion latent space via multiscale windowed cross attention and FiLM conditioning, with gated residuals preventing overcorrection. This design enables multi-task learning across two complementary objectives: (a) SAR-to-optical translation for fine-grained RGB reconstruction and (b) flood water region segmentation for hydrologic interpretation. The dual decoders are optimized using Charbonnier SSIM for structural fidelity, edge FFT magnitude losses for spectral realism, and Dice BCE hydrology-aware edge alignment for precise flood water delineation. A feature distillation constraint further aligns fused SAR features with the optical teacher's manifold. Evaluations on SEN1FLOODS11, DEEPFLOOD, and SEN12MS demonstrate that FLoRA surpasses fusion baselines in PSNR, SSIM, and LPIPS, demonstrating that multi-modal fusion within a teacher-guided latent space yields semantically faithful and physically consistent flood-water intelligence from spaceborne observations.

Related papers

• Rethinking Conditional Generation for Underwater Salient Object Detection [69.38806804258452]
  A Conditional Generation Network (DCGNet) is designed to construct reliable conditional features for underwater saliency generation.<n>DCGNet significantly outperforms existing state-of-the-art methods, verifying its potential for complex underwater visual applications.
  arXiv  Detail & Related papers  (2026-07-02T07:48:06Z)
• High-Resolution Flood Mapping With Sentinel-1 and Sentinel-2 via Misalignment-Robust Cross-Sensor Learning and Generative Despeckling [1.458346800173785]
  This work presents an integrated flood mapping framework that jointly addresses limitations through curated and novel learning strategies.<n>We introduce a new Sentinel-2 (S2) and Sentinel-1 (S1) dataset covering the contiguous United States, featuring pixel-accurate 10 m water masks with emphasis on challenging weather conditions and urban environments that are underrepresented in existing benchmarks.<n>Experiments using UNet and UNet++ architectures demonstrate strong multispectral performance (AUPRC up to 0.956) and statistically significant improvements in SAR flood mapping when using shift-invariant loss and CVAE-based despeckling.
  arXiv  Detail & Related papers  (2026-06-29T16:19:59Z)
• IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal [18.935012033334477]
  We propose an Information Bottleneck-driven High-Fidelity Network (IB-HFN) for SAR-assisted optical cloud removal.<n>IB-HFN employs a dual-stream backbone to preserve modality-specific representations before deep semantic fusion.<n> Experiments on the SEN12MS-CR dataset under challenging-temporal splits demonstrate that IB-HFN achieves superior structural preservation and spectral fidelity over existing methods.
  arXiv  Detail & Related papers  (2026-06-08T11:19:06Z)
• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval [54.741349848771144]
  GeoMamba is a geometry-driven framework for optical-SAR fine-grained retrieval.<n>GFI module enhances cross-modal feature interaction and incorporates structural priors.<n>GeoMamba outperforms existing methods, achieving 63.3% mAP and 77.0% Rank-1 accuracy in all-to-all retrieval setting.
  arXiv  Detail & Related papers  (2026-05-19T12:08:09Z)
• Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification [44.20355634294824]
  Representative Spectral Correlation Network (RSCNet) is a novel multi-source image classification framework.<n>RSCNet addresses challenges through spectral selection and adaptive interaction.<n> Experiments on three public benchmark datasets demonstrate that RSCNet achieves superior performance compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-30T02:10:38Z)
• Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation [26.524834042246408]
  Fog and strong reflections in maritime environments cause severe image degradation.<n>Fog and strong reflections in maritime environments cause severe image degradation.<n>The Infrared-Visible Maritime Ship dataset is proposed to cover various maritime scenarios under diverse weather and illumination conditions.<n>The framework includes a Frequency-Spatial Enhancement Complementary (FSEC) module for degradation suppression and structural enhancement, a Semantic-Visual Consistency Attention (SVCA) module for semantic-consistent guidance, and a cross-modality guided attention mechanism for selective fusion.
  arXiv  Detail & Related papers  (2026-03-30T13:26:05Z)
• Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation [11.468907022707013]
  TDP-CR is a task-driven framework that jointly performs cloud removal and land-cover segmentation.<n>Central to our approach is a Prompt-Guided Fusion mechanism, which utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty.<n>Experiments on the LuojiaSET-OSFCR dataset demonstrate the superiority of our framework.
  arXiv  Detail & Related papers  (2026-01-17T13:32:38Z)
• MOS: Mitigating Optical-SAR Modality Gap for Cross-Modal Ship Re-Identification [7.7794453452329]
  Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery has emerged as a critical yet underexplored task in maritime intelligence and surveillance.<n>We propose MOS, a novel framework designed to mitigate the optical-SAR modality gap and achieve modality-consistent feature learning for optical-SAR cross-modal ship ReID.
  arXiv  Detail & Related papers  (2025-12-03T03:23:19Z)
• S2ML: Spatio-Spectral Mutual Learning for Depth Completion [56.26679539288063]
  Raw depth images captured by RGB-D cameras often suffer from incomplete depth values due to weak reflections, boundary shadows, and artifacts.<n>Existing methods address this problem through depth completion in the image domain, but they overlook the physical characteristics of raw depth images.<n>We propose a Spatio-Spectral Mutual Learning framework (S2ML) to harmonize the advantages of both spatial and frequency domains for depth completion.
  arXiv  Detail & Related papers  (2025-11-08T15:01:55Z)
• Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images [51.74614065919118]
  This paper introduces SegEarth-OV, the first framework for annotation-free open-vocabulary segmentation of RS images.<n>We propose SimFeatUp, a universal upsampler that robustly restores high-resolution spatial details from coarse features.<n>We also present a simple yet effective Global Bias Alleviation operation to subtract the inherent global context from patch features.
  arXiv  Detail & Related papers  (2025-08-25T14:22:57Z)
• DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization [43.974708665104565]
  We introduce DepthMatch, a semi-supervised learning framework that is specifically designed for RGB-D scene parsing.<n>We propose complementary patch mix-up augmentation to explore the latent relationships between texture and spatial features in RGB-D image pairs.<n>We also design a lightweight spatial prior injector to replace traditional complex fusion modules, improving the efficiency of heterogeneous feature fusion.
  arXiv  Detail & Related papers  (2025-05-26T14:26:31Z)
• DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model [27.83437788159158]
  We introduce DehazeMamba, a novel SAR-guided dehazing network built on a progressive haze decoupling fusion strategy.<n>Our approach incorporates two key innovations: a Haze Perception and Decoupling Module (HPDM) that dynamically identifies haze-affected regions through optical-SAR difference analysis, and a Progressive Fusion Module (PFM) that mitigates domain shift through a two-stage fusion process based on feature quality assessment.<n>Extensive experiments demonstrate that DehazeMamba significantly outperforms state-of-the-art methods, achieving a 0.73 dB improvement in PSNR and substantial enhancements in downstream tasks such as
  arXiv  Detail & Related papers  (2025-03-17T11:25:05Z)
• DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation [44.99833362998488]
  We present DeepAqua, a self-supervised deep learning model that eliminates the need for manual annotations during the training phase. We exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%.
  arXiv  Detail & Related papers  (2023-05-02T18:06:21Z)
• Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images [193.77450545067967]
  We propose an end-to-end Dense Attention Fluid Network (DAFNet) for salient object detection in optical remote sensing images (RSIs) A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships. We construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations.
  arXiv  Detail & Related papers  (2020-11-26T06:14:10Z)

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.