HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918510535376896 |
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| author | Das, Aryan Rachamalla, Tanishq Singh, Pravendra Biswas, Koushik Verma, Vinay Kumar Garcia, Salvador Plaza, Antonio Roy, Swalpa Kumar |
| author_facet | Das, Aryan Rachamalla, Tanishq Singh, Pravendra Biswas, Koushik Verma, Vinay Kumar Garcia, Salvador Plaza, Antonio Roy, Swalpa Kumar |
| contents | We introduce HyperCap, the first large-scale hyperspectral captioning dataset designed to enhance model performance and effectiveness in remote sensing applications. Unlike traditional hyperspectral imaging (HSI) benchmarks, HyperCap integrates spectral data with pixel-wise textual annotations, enabling deeper semantic understanding. This dataset enhances model performance in tasks like classification and feature extraction, providing a valuable resource for advanced remote sensing applications. HyperCap is constructed from four benchmark datasets and annotated through a hybrid approach combining automated and manual methods to ensure accuracy and consistency. Empirical evaluations using state-of-the-art encoders and diverse fusion techniques demonstrate significant improvements in classification performance. These results underscore the potential of vision-language learning in HSI and position HyperCap as a foundational dataset for future research in the field. The code and dataset are available at https://github.com/arya-domain/HyperCap. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_12217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models Das, Aryan Rachamalla, Tanishq Singh, Pravendra Biswas, Koushik Verma, Vinay Kumar Garcia, Salvador Plaza, Antonio Roy, Swalpa Kumar Computer Vision and Pattern Recognition We introduce HyperCap, the first large-scale hyperspectral captioning dataset designed to enhance model performance and effectiveness in remote sensing applications. Unlike traditional hyperspectral imaging (HSI) benchmarks, HyperCap integrates spectral data with pixel-wise textual annotations, enabling deeper semantic understanding. This dataset enhances model performance in tasks like classification and feature extraction, providing a valuable resource for advanced remote sensing applications. HyperCap is constructed from four benchmark datasets and annotated through a hybrid approach combining automated and manual methods to ensure accuracy and consistency. Empirical evaluations using state-of-the-art encoders and diverse fusion techniques demonstrate significant improvements in classification performance. These results underscore the potential of vision-language learning in HSI and position HyperCap as a foundational dataset for future research in the field. The code and dataset are available at https://github.com/arya-domain/HyperCap. |
| title | HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.12217 |