Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges
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| Format: | Preprint |
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2025
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| _version_ | 1866912797029302272 |
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| author | Chang, Yu-Tang Chen, Pin-Wei Chen, Shih-Fang |
| author_facet | Chang, Yu-Tang Chen, Pin-Wei Chen, Shih-Fang |
| contents | Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory. While foundation models pre-trained on large remote sensing datasets show promise, their learned representations often fail to transfer to domain-specific applications like close-range agricultural monitoring where spectral signatures, spatial scales, and semantic targets differ fundamentally. This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation that learns global clustering structure from local patch observations without pre-training. DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware while maintaining constant memory usage. On a leaf disease dataset, DGC achieves background-tissue separation (mean IoU 0.925) and demonstrates unsupervised disease detection through navigable semantic granularity. However, the framework suffers from optimization instability rooted in multi-objective loss balancing: meaningful representations emerge rapidly but degrade due to cluster over-merging in feature space. We position this work as intellectual scaffolding - the design philosophy has merit, but stable implementation requires principled approaches to dynamic loss balancing. Code and data are available at https://github.com/b05611038/HSI_global_clustering. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_24172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges Chang, Yu-Tang Chen, Pin-Wei Chen, Shih-Fang Computer Vision and Pattern Recognition Machine Learning 68T05, 68T10, 62H30 I.4.6; I.5.3; I.2.6 Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory. While foundation models pre-trained on large remote sensing datasets show promise, their learned representations often fail to transfer to domain-specific applications like close-range agricultural monitoring where spectral signatures, spatial scales, and semantic targets differ fundamentally. This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation that learns global clustering structure from local patch observations without pre-training. DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware while maintaining constant memory usage. On a leaf disease dataset, DGC achieves background-tissue separation (mean IoU 0.925) and demonstrates unsupervised disease detection through navigable semantic granularity. However, the framework suffers from optimization instability rooted in multi-objective loss balancing: meaningful representations emerge rapidly but degrade due to cluster over-merging in feature space. We position this work as intellectual scaffolding - the design philosophy has merit, but stable implementation requires principled approaches to dynamic loss balancing. Code and data are available at https://github.com/b05611038/HSI_global_clustering. |
| title | Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T05, 68T10, 62H30 I.4.6; I.5.3; I.2.6 |
| url | https://arxiv.org/abs/2512.24172 |