Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution

Hyperspectral Remote Sensing Imagery Generation From RGB Images Based on Joint Discrimination

“…In addition to the previous means, more HSI SR methods use other images with high spatial resolution and make full use of the spatial information [34,35]. On this basis, some research base on the sparsity of HSI [21,24,[35][36][37][38] to reconstruct the HR-HSI; some authors study on the selfsimilarity between local and nonlocal patches [25,[34][35][36] and the low rank of them [38][39][40]; some literatures have use or model the imaging principles and degradation process for super-resolution [24,41,42]. From the perspective of the solution process: dictionary-based methods are common such as [25,38,39].…”

“…A typical way to improve the spatial resolution of HSI is hyperspectral super-resolution (HSI SR) [20,21]. The SR methods can be classified into two representative categories, one is SR only use the low-resolution HSI (LR-HSI) [22,23], another contains the methods that enhance the spatial resolution of HSI with the spatial information in high-resolution RGB or multispectral images (MSI) [24,25]. The HSI SR can improve the spatial resolution on the basis of existing HSI.…”

A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification

“…OWADAYS, the hyperspectral images (HSIs) with high spectral resolution have attracted much attention in the field of remote sensing [1], [2]. Since these images have hundreds of continuous observation bands across the entire electromagnetic spectrum, more spectral information can be obtained when they are used.…”

Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

“…(1) extensions of MS pan-sharpening approaches [2,3], (2) Bayesian-based approaches [4,5], (3) matrix factorization or tensor-based approaches [6][7][8][9], (4) deep learning (DL)-based approaches [10][11][12][13], and (5) others [14][15][16]. Detailed analyses of HS image fusion techniques can be found in [17][18][19].…”