Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-
scene classification, samples between source and target scenes are not drawn from the …

Cross-scene hyperspectral image classification (HSIC) is a challenging topic in remote
sensing, especially when there are no labels in the target domain. Domain adaptation (DA) …

Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI)
classification when abundant labeled samples are available. The problem is that HSI …

Deep domain adaptation has achieved promising results in cross-domain hyperspectral
image (HSI) classification. However, existing methods often focus on aligning data …

The task of hyperspectral image (HSI) classification is fundamental and crucial in HSI
processing. Currently, domain adaptive methods have become a research hotspot in HSI …

Recent developments and research in modern machine learning have led to substantial
improvements in the geospatial field. Although numerous deep learning models have been …

For hyperspectral cross-domain recognition applications, the unseen target domain (TD) is
inevitable, and the model can only be trained on the source domain (SD) but directly applied …

Limited labeled training samples constitute a challenge in hyperspectral image
classification, with much research devoted to cross-domain adaptation, where the classes of …

With the development of observation technology, multispectral (MS) images of large scenes
are easy to obtain, but the low spectral resolution limits their classification ability. Moreover …