Aug 2026· 2026 2nd International Conference on Electronic Information, Computer and Aerospace Remote Sensing (EICARS)· pp. 53-57· 0 citations· 10 references
Abstract
Multimodal remote sensing image matching is challenged by nonlinear radiometric differences, geometric deformation, sensor noise, and weak cross-modal feature repeatability. We present an edge-aware coarse-to-fine Transformer network. A Feature Enhancement Transformer (FET) performs linear self- and cross-attention, while a Multiscale Edge Enhancement Module (MEEM) injects stable boundary cues and suppresses modality-specific interference. Bidirectional confidence and mutual-nearest-neighbor filtering select reliable coarse matches, followed by local correlation refinement. On optical-depth, infrared-optical, optical-map, and synthetic aperture radar (SAR)-optical test groups, the method achieves an average root-mean-square error (RMSE) of 1.38 pixels, 776.3 correct matches, and a runtime of 0.28 s. It nearly doubles the second-best correct-match count while retaining Local Feature Transformer (LoFTR)-level accuracy and subsecond efficiency.
In remote-sensing scene classification (RSSC), persistent challenges such as high interclass similarity and substantial intraclass diversity remain key obstacles to accurate recognition. Although vision Transformer (ViT) has demonstrated outstanding performance, it tends to smooth out high-frequency discriminative deta...
Hui-Hui Dong, Tong Wang, Zong-Fang Ma et al.· IEEE Transactions on Geoscie...· 0 citations
Multimodal object detection can improve detection accuracy and robustness by fusing complementary information from different modalities. However, in remote sensing scenarios, complex environments often weaken data reliability and cross-modal complementarity, making fixed-pattern fusion strategies insufficient for robus...
Jian-Ning Chi, Fei-Hu Yin, Qing-Wang Wang et al.· IEEE Transactions on Geoscie...· 0 citations
A Lighting-Aware Spatial–Frequency Fusion Network (LASFNet) for security surveillance, which balances target saliency and scene structure, providing more effective visual input for object detection under complex illumination.
Yi Jiang, Xin Nie, Imad Rida· Algorithms· 0 citations
Multimodal remote sensing object detection benefits from the complementary characteristics of visible and infrared modalities. However, owing to the heterogeneous imaging mechanisms of RGB and infrared sensors, feature representations from the two modalities progressively become semantically inconsistent during hie...
Synthetic aperture radar (SAR) target detection is a fundamental task in intelligent SAR image interpretation and supports a wide range of remote sensing applications. However, existing methods remain limited in handling sparse and discontinuous scattering responses, substantial variations in target scale, speckle nois...
An Chang, Yu Zhang, Chang Liu· Remote Sensing· 0 citations
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