A Hierarchical Semantic Consistency Constraint Framework for Hyperspectral and LiDAR Data Joint Classification.
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| Title: | A Hierarchical Semantic Consistency Constraint Framework for Hyperspectral and LiDAR Data Joint Classification. |
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| Authors: | Shen, Jie1,2 (AUTHOR), Ma, Yimeng1,2 (AUTHOR), Yang, Houqun1,2 (AUTHOR) yhq@hainanu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2058. 26p. |
| Subjects: | Multisensor data fusion, Remote sensing, Resemblance (Philosophy), Remote-sensing images, Land use mapping |
| Geographic Terms: | Augsburg (Germany) |
| Abstract: | Highlights: What are the main findings? A hierarchical semantic consistency constraint framework (HSCC) is proposed for joint classification of HSI and LiDAR data. The framework progressively strengthens cross-modal interaction through a progressive interactive fusion network (PIFNet) and introduces a semantic consistency constraint (SCC) strategy to explicitly enforce feature similarity for the same object across different modalities and levels, effectively mitigating semantic drift. What are the implications of the main findings? HSCC achieves state-of-the-art classification performance on three public datasets, providing a high-accuracy solution for multi-source remote sensing data fusion in complex land-cover scenes. HSCC offers a hierarchical semantic alignment paradigm for heterogeneous feature fusion of multi-source remote sensing data, which can be extended to other multimodal tasks. Hyperspectral image (HSI) and LiDAR data fusion is valuable for land-cover classification in complex surface scenes. Existing methods typically extract features from each modality independently and then consider how to fuse them, ignoring the semantic consistency between features of different modalities and across different hierarchical levels. Moreover, fully mining and exploiting the complementary information between multimodal remote sensing data remains a critical issue. To address these challenges, this paper proposes a hierarchical semantic consistency constraint (HSCC) framework for HSI and LiDAR data joint classification. The framework is co-constructed by a progressive interactive fusion network (PIFNet) and a semantic consistency constraint (SCC) strategy. Specifically, PIFNet progressively calibrates the semantic representations of multimodal features at different abstraction levels through Cross-Modal Shared Attention and Symmetric Cross-Attention mechanisms, promoting information parity in deep interactions. The SCC strategy establishes multi-level semantic associations and employs a semantic consistency constraint loss to guide the network to autonomously maintain the consistency of the same land-cover object across heterogeneous feature representations, thereby further enhancing the discriminative power of the fused features. Experiments on three public datasets, MUUFL, Houston2013, and Augsburg, demonstrate that HSCC outperforms current state-of-the-art methods, validating its effectiveness in multi-source remote sensing data fusion classification tasks. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A hierarchical semantic consistency constraint framework (HSCC) is proposed for joint classification of HSI and LiDAR data. The framework progressively strengthens cross-modal interaction through a progressive interactive fusion network (PIFNet) and introduces a semantic consistency constraint (SCC) strategy to explicitly enforce feature similarity for the same object across different modalities and levels, effectively mitigating semantic drift. What are the implications of the main findings? HSCC achieves state-of-the-art classification performance on three public datasets, providing a high-accuracy solution for multi-source remote sensing data fusion in complex land-cover scenes. HSCC offers a hierarchical semantic alignment paradigm for heterogeneous feature fusion of multi-source remote sensing data, which can be extended to other multimodal tasks. Hyperspectral image (HSI) and LiDAR data fusion is valuable for land-cover classification in complex surface scenes. Existing methods typically extract features from each modality independently and then consider how to fuse them, ignoring the semantic consistency between features of different modalities and across different hierarchical levels. Moreover, fully mining and exploiting the complementary information between multimodal remote sensing data remains a critical issue. To address these challenges, this paper proposes a hierarchical semantic consistency constraint (HSCC) framework for HSI and LiDAR data joint classification. The framework is co-constructed by a progressive interactive fusion network (PIFNet) and a semantic consistency constraint (SCC) strategy. Specifically, PIFNet progressively calibrates the semantic representations of multimodal features at different abstraction levels through Cross-Modal Shared Attention and Symmetric Cross-Attention mechanisms, promoting information parity in deep interactions. The SCC strategy establishes multi-level semantic associations and employs a semantic consistency constraint loss to guide the network to autonomously maintain the consistency of the same land-cover object across heterogeneous feature representations, thereby further enhancing the discriminative power of the fused features. Experiments on three public datasets, MUUFL, Houston2013, and Augsburg, demonstrate that HSCC outperforms current state-of-the-art methods, validating its effectiveness in multi-source remote sensing data fusion classification tasks. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122058 |
