TL;DR
A cascaded optimization of FOV alignment, coarse registration, and non-rigid transformation, which achieves high-precision cross-modal image alignment and overcomes feature sparsity and geometric distortion in multi-modal images of water scenes.
To address challenges in multimodal registration of synthetic aperture radar and optical images—including nonlinear radiation differences, multiplicative speckle noise, and large geometric rotations—we propose a robust registration method based on a local orientation scale contour (LOSC) descriptor. First, an orientati...
Object detection in low-resolution optical-SAR remote sensing imagery is challenged by degraded object structures, residual cross-modal spatial inconsistency, and unreliable SAR responses. To address these issues, we propose TPA-NET, a progressive cross-modal alignment and confidence fusion network built on YOLOv10. TP...
Accurate dam detection in high-resolution remote sensing imagery is challenging because dams exhibit substantial variations in scale and morphology, indistinct boundaries, and high visual similarity to roads, bridges, and shorelines. Existing single-modal detection methods rely primarily on spectral and texture informa...
Traditional image registration methods rely on handcrafted feature extraction and matching strategies, which are inherently subjective and fail to fully exploit data-driven mechanisms. To address these limitations, we propose a novel dual-channel network for optical and synthetic aperture radar image registration. Our...
The proposed speckle-assisted binocular 3D reconstruction method based on a multi-scale adaptive feature fusion algorithm offers a reliable technical solution for efficient texture-depth analysis and practical pavement condition assessment.
The Intermodal Dual-MAE Framework with a Similarity-based RL Masking Strategy (SRLM) is proposed, which adaptively masks informative positions by leveraging cross-modal similarity and reinforcement learning, thus narrowing the modality gap.
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