Aug 2026· International Conference on Optoelectronic Information and Computer Engineering (OICE)· Vol 14317, pp. 143170T - 143170T-11· 0 citations· 17 references
Engineering
TL;DR
PG-SDNet operationalizes continuum-removal principles by decomposing each spectrum into a slowly varying smooth component and a complementary absorption component, showing that explicit physical decomposition can improve sample efficiency, generalization, and interpretability in HSI classification without sacrificing discriminative performance.
Hyperspectral images (HSIs) provide rich spectral information, offering unique advantages for fine-grained land-cover classification. However, HSI classification remains challenged by insufficient spectral–spatial feature exploitation and significant variations in class difficulty under limited labeled samples. To addr...
Jin Zhang, Ying Cui, Li-Guo Wang et al.· IEEE Geoscience and Remote S...· 0 citations
Hyperspectral object tracking leverages abundant spectral information to provide unique advantages for target discrimination in complex scenes. However, existing methods typically treat hyperspectral images as multi-channel extensions of RGB images, performing feature fusion in fixed band order. This approach leads to...
Ju-Liu Li, Han-Lin Qin, Shuo-Wen Yang et al.· 0 citations
Hyperspectral remote sensing imagery provides dense spectral measurements that support material identification and fine-grained classification, but it is expensive to acquire and often limited in spatial resolution. On the contrary, RGB imagery is low-cost and easy to capture with rich spatial details, yet its few chan...
Hui-Jie Jia, Chen-Lin Wu, Min Huang et al.· IEEE Transactions on Geoscie...· 0 citations
Recently, self-attention mechanisms have significantly advanced hyperspectral image classification (HSIC) by enabling effective modeling of long-range spatial–spectral dependencies. However, existing methods often suffer from blurred land-cover boundaries and insufficient discriminability between spectrally similar cla...
Zhen-Qiu Shu, Nan-Bo Liu, Ke-Xin Zeng et al.· IEEE Transactions on Geoscie...· 0 citations
Recovering hyperspectral images (HSIs) from RGB observations is a highly ill-posed problem due to severe spectral information loss. However, current methods either rely on costly paired RGB–HSI datasets that are difficult to obtain or on unpaired RGB–HSI data for spectral guidance, which increases training costs and of...
Ningjia Lv, Feiwang Yuan, Wei He et al.· IEEE Transactions on Geoscie...· 0 citations
The results suggest that decoupled spatial–spectral representation and adaptive multi-scale fusion can improve hyperspectral image classification under the evaluated benchmark settings.
