104 research outputs found
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure
Deep Learning based data-fusion methods for remote sensing applications
In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks
Source-Aware Spatial-Spectral-Integrated Double U-Net for Image Fusion
In image fusion tasks, pictures from different sources possess distinctive
properties, therefore treating them equally will lead to inadequate feature
extracting. Besides, multi-scaled networks capture information more
sufficiently than single-scaled models in pixel-wised problems. In light of
these factors, we propose a source-aware spatial-spectral-integrated double
U-shaped network called Net. The network is mainly composed of a
spatial U-net and a spectral U-net, which learn spatial details and spectral
characteristics discriminately and hierarchically. In contrast with most
previous works that simply apply concatenation to integrate spatial and
spectral information, a novel structure named the spatial-spectral block
(called Block) is specially designed to merge feature maps from
different sources effectively. Experiment results show that our method
outperforms the representative state-of-the-art (SOTA) approaches in both
quantitative and qualitative evaluations for a variety of image fusion
missions, including remote sensing pansharpening and hyperspectral image
super-resolution (HISR)
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
Guiding 3D U-nets with signed distance fields for creating 3D models from images
Morphological analysis of the left atrial appendage is an important tool to
assess risk of ischemic stroke. Most deep learning approaches for 3D
segmentation is guided by binary labelmaps, which results in voxelized
segmentations unsuitable for morphological analysis. We propose to use signed
distance fields to guide a deep network towards morphologically consistent 3D
models. The proposed strategy is evaluated on a synthetic dataset of simple
geometries, as well as a set of cardiac computed tomography images containing
the left atrial appendage. The proposed method produces smooth surfaces with a
closer resemblance to the true surface in terms of segmentation overlap and
surface distance.Comment: MIDL 2019 [arXiv:1907.08612
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
- …