Keywords
Abstract
Remote sensing image interpretation requires the integration of semantic encoding, visual restoration, hyperspectral-LiDAR fusion, and efficient vision-language representation. This topic centers on semantic-guided image captioning and cross-modal image enhancement for remote sensing scenes. Dual-path pre-fusion supports the alignment of visual and semantic cues in caption generation, while hyperspectral and LiDAR fusion improves spatial-spectral representation for land-cover and scene understanding. Instance-aware super-resolution enhances real-world visual details, and token-efficient forensic vision-language modeling contributes methods for compressing multimodal evidence without losing semantic information. Medical image studies provide related examples of low-dose image enhancement, melanoma detection, and DCE-MRI segmentation, showing how visual restoration and semantic reasoning can generalize across high-stakes visual domains.
References
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