4 research outputs found
Operational Neural Networks for Efficient Hyperspectral Single-Image Super-Resolution
Hyperspectral Imaging is a crucial tool in remote sensing which captures far
more spectral information than standard color images. However, the increase in
spectral information comes at the cost of spatial resolution. Super-resolution
is a popular technique where the goal is to generate a high-resolution version
of a given low-resolution input. The majority of modern super-resolution
approaches use convolutional neural networks. However, convolution itself is a
linear operation and the networks rely on the non-linear activation functions
after each layer to provide the necessary non-linearity to learn the complex
underlying function. This means that convolutional neural networks tend to be
very deep to achieve the desired results. Recently, self-organized operational
neural networks have been proposed that aim to overcome this limitation by
replacing the convolutional filters with learnable non-linear functions through
the use of MacLaurin series expansions. This work focuses on extending the
convolutional filters of a popular super-resolution model to more powerful
operational filters to enhance the model performance on hyperspectral images.
We also investigate the effects that residual connections and different
normalization types have on this type of enhanced network. Despite having fewer
parameters than their convolutional network equivalents, our results show that
operational neural networks achieve superior super-resolution performance on
small hyperspectral image datasets.Comment: 12 pages, 7 figure
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to
generate a high-resolution (HR) HSI with higher spectral and spatial fidelity
from its low-resolution (LR) counterpart. The generative adversarial network
(GAN) has proven to be an effective deep learning framework for image
super-resolution. However, the optimisation process of existing GAN-based
models frequently suffers from the problem of mode collapse, leading to the
limited capacity of spectral-spatial invariant reconstruction. This may cause
the spectral-spatial distortion on the generated HSI, especially with a large
upscaling factor. To alleviate the problem of mode collapse, this work has
proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can
map the generated spectral-spatial features from the image space to the latent
space and produce a coupling component to regularise the generated samples.
Essentially, we treat an HSI as a high-dimensional manifold embedded in a
latent space. Thus, the optimisation of GAN models is converted to the problem
of learning the distributions of high-resolution HSI samples in the latent
space, making the distributions of the generated super-resolution HSIs closer
to those of their original high-resolution counterparts. We have conducted
experimental evaluations on the model performance of super-resolution and its
capability in alleviating mode collapse. The proposed approach has been tested
and validated based on two real HSI datasets with different sensors (i.e.
AVIRIS and UHD-185) for various upscaling factors and added noise levels, and
compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR,
BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure
Radiometrically-Accurate Hyperspectral Data Sharpening
Improving the spatial resolution of hyperpsectral image (HSI) has traditionally been an important topic in the field of remote sensing. Many approaches have been proposed based on various theories including component substitution, multiresolution analysis, spectral unmixing, Bayesian probability, and tensor representation. However, these methods have some common disadvantages, such as that they are not robust to different up-scale ratios and they have little concern for the per-pixel radiometric accuracy of the sharpened image. Moreover, many learning-based methods have been proposed through decades of innovations, but most of them require a large set of training pairs, which is unpractical for many real problems. To solve these problems, we firstly proposed an unsupervised Laplacian Pyramid Fusion Network (LPFNet) to generate a radiometrically-accurate high-resolution HSI. First, with the low-resolution hyperspectral image (LR-HSI) and the high-resolution multispectral image (HR-MSI), the preliminary high-resolution hyperspectral image (HR-HSI) is calculated via linear regression. Next, the high-frequency details of the preliminary HR-HSI are estimated via the subtraction between it and the CNN-generated-blurry version. By injecting the details to the output of the generative CNN with the low-resolution hyperspectral image (LR-HSI) as input, the final HR-HSI is obtained. LPFNet is designed for fusing the LR-HSI and HR-MSI covers the same Visible-Near-Infrared (VNIR) bands, while the short-wave infrared (SWIR) bands of HSI are ignored. SWIR bands are equally important to VNIR bands, but their spatial details are more challenging to be enhanced because the HR-MSI, used to provide the spatial details in the fusion process, usually has no SWIR coverage or lower-spatial-resolution SWIR. To this end, we designed an unsupervised cascade fusion network (UCFNet) to sharpen the Vis-NIR-SWIR LR-HSI. First, the preliminary high-resolution VNIR hyperspectral image (HR-VNIR-HSI) is obtained with a conventional hyperspectral algorithm. Then, the HR-MSI, the preliminary HR-VNIR-HSI, and the LR-SWIR-HSI are passed to the generative convolutional neural network to produce an HR-HSI. In the training process, the cascade sharpening method is employed to improve stability. Furthermore, the self-supervising loss is introduced based on the cascade strategy to further improve the spectral accuracy. Experiments are conducted on both LPFNet and UCFNet with different datasets and up-scale ratios. Also, state-of-the-art baseline methods are implemented and compared with the proposed methods with different quantitative metrics. Results demonstrate that proposed methods outperform the competitors in all cases in terms of spectral and spatial accuracy