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Primary Modality Guided Multimodal Change Detection | IEEE Conference Publication | IEEE Xplore

Primary Modality Guided Multimodal Change Detection


Abstract:

Multimodal images can provide richer information for a wide range of applications. However, the physical heterogeneity resulted by the difference of spatial resolution po...Show More

Abstract:

Multimodal images can provide richer information for a wide range of applications. However, the physical heterogeneity resulted by the difference of spatial resolution pose great challenges for multimodal change detection. To this end, we propose a change detection method called primary modality guided deep neural network (PMGN), integrating multi-resolution and multimodal data. First, we propose the principal modality and rely more on its information. Second, PMGN compensates for the limitations of low spatial resolution modalities through the primary modality guided feature exchange module. Finally, the adaptive decision fusion module enables the multimodal decision-level features to fuse efficiently. Experiments demonstrate the effectiveness and advantages of the proposed approach.
Date of Conference: 07-12 July 2024
Date Added to IEEE Xplore: 05 September 2024
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Conference Location: Athens, Greece

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