… hyperspectral image classification. In this article, to tackle the issues of end-to-end feature
learning and transfer learning … a unified multiscale learning (UML) framework, which is based …

… supervised 3-D deep learning framework for spectral–spatial representation learning and
HSI classification… The designed SSRN, which contains consecutive spectral and spatial …

… , a novel spectral–spatial deep learning framework for hyperspectral image classification is
… In this section, a joint spectral–spatial classification framework is constructed by integrating …

… In addition, the GAN [45] was also adopted to construct semisupervised feature learning
framework for HSI classification [46], [59]. In such works, the generator created fake …

… Deep learning methods for HSI classification usually follow a patchwise learning framework.
Recently, a fast patch-free global learning (FPGA) architecture was proposed for HSI classi…

… To tackle the above problems, an interactive learning framework inspired by ViT is … interactive
learning (CSIL) framework. Different from existing works, the CSIL framework enables to …

… During recent years, convolutional neural network (CNN)-based methods have been widely
applied to hyperspectral image (HSI) classification by mostly mining the spectral variabilities…

… 13 So far, most hyperspectral image processing methods are … consider the image as an
ensemble of spectral measurements … -supervised hyperspectral image classification framework in …

… an AL framework for hyperspectral image classification in which spectral and spatial
information are combined in two different ways. The flowchart of the proposed AL framework is …

… Among the deep learning-based methods, convolutional neural networks (… spectral–spatial
feature representation learning frameworks, have been widely used in HSI classification [25]–[…