1,389 research outputs found
Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency
Hyperspectral images (HSI) with abundant spectral information reflected
materials property usually perform low spatial resolution due to the hardware
limits. Meanwhile, multispectral images (MSI), e.g., RGB images, have a high
spatial resolution but deficient spectral signatures. Hyperspectral and
multispectral image fusion can be cost-effective and efficient for acquiring
both high spatial resolution and high spectral resolution images. Many of the
conventional HSI and MSI fusion algorithms rely on known spatial degradation
parameters, i.e., point spread function, spectral degradation parameters,
spectral response function, or both of them. Another class of deep
learning-based models relies on the ground truth of high spatial resolution HSI
and needs large amounts of paired training images when working in a supervised
manner. Both of these models are limited in practical fusion scenarios. In this
paper, we propose an unsupervised HSI and MSI fusion model based on the cycle
consistency, called CycFusion. The CycFusion learns the domain transformation
between low spatial resolution HSI (LrHSI) and high spatial resolution MSI
(HrMSI), and the desired high spatial resolution HSI (HrHSI) are considered to
be intermediate feature maps in the transformation networks. The CycFusion can
be trained with the objective functions of marginal matching in single
transform and cycle consistency in double transforms. Moreover, the estimated
PSF and SRF are embedded in the model as the pre-training weights, which
further enhances the practicality of our proposed model. Experiments conducted
on several datasets show that our proposed model outperforms all compared
unsupervised fusion methods. The codes of this paper will be available at this
address: https: //github.com/shuaikaishi/CycFusion for reproducibility
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
Deep Learning Approaches for Seagrass Detection in Multispectral Imagery
Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It also introduces novel domain adaptation approaches to adapt the models for new locations and for temporal image series. In Chapter 3, I compare a deep capsule network (DCN) with a deep convolutional neural network (DCNN) for seagrass detection in high-resolution multispectral satellite images. These methods are tested on three satellite images in Florida coastal areas and obtain comparable performances. In addition, I also propose a few-shot deep learning strategy to transfer knowledge learned by DCN from one location to the others for seagrass detection. In Chapter 4, I develop a semi-supervised domain adaptation method to generalize a trained DCNN model to multiple locations for seagrass detection. First, the model utilizes a generative adversarial network (GAN) to align marginal distribution of data in the source domain to that in the target domain using unlabeled data from both domains. Second, it uses a few labeled samples from the target domain to align class-specific data distributions between the two. The model achieves the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods. In Chapter 5, I develop a semantic segmentation method for seagrass detection in multispectral time-series images. First, I train a state-of-the-art image segmentation method using an active learning approach where I use the DCNN classifier in the loop. Then, I develop an unsupervised domain adaptation (UDA) algorithm to detect seagrass across temporal images. I also extend our unsupervised domain adaptation work for seagrass detection across locations. In Chapter 6, I present an automated bathymetry estimation model based on multispectral satellite images. Bathymetry refers to the depth of the ocean floor and contributes a predominant role in identifying marine species in seawater. Accurate bathymetry information of coastal areas will facilitate seagrass detection by reducing false positives because seagrass usually do not grow beyond a certain depth. However, bathymetry information of most parts of the world is obsolete or missing. Traditional bathymetry measurement systems require extensive labor efforts. I utilize an ensemble machine learning-based approach to estimate bathymetry based on a few in-situ sonar measurements and evaluate the proposed model in three coastal locations in Florida
A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Image super-resolution (SR) is one of the vital image processing methods that
improve the resolution of an image in the field of computer vision. In the last
two decades, significant progress has been made in the field of
super-resolution, especially by utilizing deep learning methods. This survey is
an effort to provide a detailed survey of recent progress in single-image
super-resolution in the perspective of deep learning while also informing about
the initial classical methods used for image super-resolution. The survey
classifies the image SR methods into four categories, i.e., classical methods,
supervised learning-based methods, unsupervised learning-based methods, and
domain-specific SR methods. We also introduce the problem of SR to provide
intuition about image quality metrics, available reference datasets, and SR
challenges. Deep learning-based approaches of SR are evaluated using a
reference dataset. Some of the reviewed state-of-the-art image SR methods
include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN),
multiscale residual network (MSRN), meta residual dense network (Meta-RDN),
recurrent back-projection network (RBPN), second-order attention network (SAN),
SR feedback network (SRFBN) and the wavelet-based residual attention network
(WRAN). Finally, this survey is concluded with future directions and trends in
SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table
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
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