Super-resolution reconstruction of hyperspectral images

Abstract Hyperspectral imaging is a versatile and powerful technology for gathering geo-data. Planes and satellites equipped with hyperspectral cameras are currently the leading contenders for large-scale imaging projects. Aiming at the shortcomings of traditional methods for detecting sparse representation of multi-spectral images, this paper proposes wireless sensor networks (WSNs) based single-hyperspectral image super-resolution method based on deep residual convolutional neural networks. We propose a different strategy that involves merging cheaper multispectral sensors to achieve hyperspectral-like spectral resolution while maintaining the WSN's spatial resolution. This method studies and mines the nonlinear relationship between low-resolution remote sensing images and high-resolution remote sensing images, constructs a deep residual convolutional neural network, connects multiple residual blocks in series, and removes some unnecessary modules. For this purpose, a decision support system is used that provides the outcome to the next layer. Finally, this paper, fully explores the similarities between natural images and hyperspectral images, use natural image samples to train convolutional neural networks, and further use migration learning to introduce the trained network model to the super-resolution problem of high-resolution remote sensing images, and solve the lack of training samples problem. A comparison between different algorithms for processing data on datasets collected in situ and via remote sensing is used to evaluate the proposed approach. The experimental results show that the method has good performance and can obtain better super-resolution effects.

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A Multi-Looking Approach for Spatial Super-Resolution on Laboratory-Based Hyperspectral Images

10.1109/igarss47720.2021.9554164 ◽

2021 ◽

Author(s):

Daniel Capella Zanotta ◽

Ademir Marques Junior ◽

Alysson Soares Aires ◽

Fabiane Bordin ◽

Graciela Racolte ◽

...

Keyword(s):

Super Resolution ◽

Hyperspectral Images

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Extended Super Resolution of Hyperspectral Images via Non-negative Sparse Coding

Sensing and Imaging ◽

10.1007/s11220-019-0235-1 ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Maneesh Pawar ◽

K. S. Venkatesh

Keyword(s):

Sparse Coding ◽

Super Resolution ◽

Hyperspectral Images

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A super-resolution reconstruction algorithm for hyperspectral images

Signal Processing ◽

10.1016/j.sigpro.2012.01.020 ◽

2012 ◽

Vol 92 (9) ◽

pp. 2082-2096 ◽

Cited By ~ 91

Author(s):

Hongyan Zhang ◽

Liangpei Zhang ◽

Huanfeng Shen

Keyword(s):

Super Resolution ◽

Reconstruction Algorithm ◽

Hyperspectral Images

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Ant colony optimization for super-resolution of hyperspectral images

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) ◽

10.1109/whispers.2016.8071672 ◽

2016 ◽

Cited By ~ 1

Author(s):

Shakti Sharma ◽

Shreya Sharma ◽

Krishna Mohan Buddhiraju

Keyword(s):

Ant Colony Optimization ◽

Super Resolution ◽

Ant Colony ◽

Hyperspectral Images

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Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

Ingeniería e Investigación ◽

10.15446/ing.investig.v36n3.54267 ◽

2016 ◽

Vol 36 (3) ◽

pp. 117

Author(s):

Miguel Angel Marquez Castellanos ◽

Cesar Augusto Vargas ◽

Henry Arguello

Keyword(s):

Hyperspectral Imaging ◽

Three Dimensional ◽

Super Resolution ◽

Accurate Estimate ◽

Hyperspectral Images ◽

Spectral Information ◽

Single Image ◽

Wide Range ◽

Spectral Algorithm ◽

Single Image Super Resolution

Hyperspectral imaging (HSI) is used in a wide range of applications such as remote sensing, space imagery, mineral detection, and exploration. Unfortunately, it is difficult to acquire hyperspectral images with high spatial and spectral resolution due to instrument limitations. The super-resolution techniques are used to reconstruct low-resolution hyperspectral images. However, traditional superresolution (SR) approaches do not allow direct use of both spatial and spectral information, which is a decisive for an optimal reconstruction. This paper proposes a single image SR algorithm for HSI. The algorithm uses the fact that the spatial and spectral information can be integrated to make an accurate estimate of the high-resolution HSI. To achieve this, two types of spatio- pectral downsampling, and a three-dimensional interpolation are proposed in order to increase coherence between the spatial and spectral information. The resulting reconstructions using the proposed method are up to 2 dB better than traditional SR approaches.

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