Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation

By Leyuan Fang, Shutao Li, Xudong Kang, Jon Atli Benediktsson. IEEE Transactions on Geoscience and Remote Sensing, 2014

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Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, methodology): “, the works [23]–[25] for HSI classification task and the works [26] and[27] for band selection task.”
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification (2021, methodology): “Many supervised classification methods based on deep learning are used to extract the spectral and spatial features, such as 3D-CNN and multiscale CNNs [17], [18].”
Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, background): “In [19] and [33]–[35], a fixed neighborhood is introduced into SRC, while in [36], the strategy of a sequence of fixed neighborhoods with different sizes for that procedure is employed.”
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (2017, background): “In [12] and [13], sparse representation was proposed to learn discriminative features from HSIs.”
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations (2018, background): “Sparse representations have also been exploited in many inverse problems in hyperspectral imaging, for example, classiﬁcation (see more details in [24– 26]).”