158 research outputs found
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
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
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
Hyperspectral pansharpening is receiving a growing interest since the last
few years as testified by a large number of research papers and challenges. It
consists in a pixel-level fusion between a lower-resolution hyperspectral
datacube and a higher-resolution single-band image, the panchromatic image,
with the goal of providing a hyperspectral datacube at panchromatic resolution.
Thanks to their powerful representational capabilities, deep learning models
have succeeded to provide unprecedented results on many general purpose image
processing tasks. However, when moving to domain specific problems, as in this
case, the advantages with respect to traditional model-based approaches are
much lesser clear-cut due to several contextual reasons. Scarcity of training
data, lack of ground-truth, data shape variability, are some such factors that
limit the generalization capacity of the state-of-the-art deep learning
networks for hyperspectral pansharpening. To cope with these limitations, in
this work we propose a new deep learning method which inherits a simple
single-band unsupervised pansharpening model nested in a sequential band-wise
adaptive scheme, where each band is pansharpened refining the model tuned on
the preceding one. By doing so, a simple model is propagated along the
wavelength dimension, adaptively and flexibly, with no need to have a fixed
number of spectral bands, and, with no need to dispose of large, expensive and
labeled training datasets. The proposed method achieves very good results on
our datasets, outperforming both traditional and deep learning reference
methods. The implementation of the proposed method can be found on
https://github.com/giu-guarino/R-PN
Probability-based Global Cross-modal Upsampling for Pansharpening
Pansharpening is an essential preprocessing step for remote sensing image
processing. Although deep learning (DL) approaches performed well on this task,
current upsampling methods used in these approaches only utilize the local
information of each pixel in the low-resolution multispectral (LRMS) image
while neglecting to exploit its global information as well as the cross-modal
information of the guiding panchromatic (PAN) image, which limits their
performance improvement. To address this issue, this paper develops a novel
probability-based global cross-modal upsampling (PGCU) method for
pan-sharpening. Precisely, we first formulate the PGCU method from a
probabilistic perspective and then design an efficient network module to
implement it by fully utilizing the information mentioned above while
simultaneously considering the channel specificity. The PGCU module consists of
three blocks, i.e., information extraction (IE), distribution and expectation
estimation (DEE), and fine adjustment (FA). Extensive experiments verify the
superiority of the PGCU method compared with other popular upsampling methods.
Additionally, experiments also show that the PGCU module can help improve the
performance of existing SOTA deep learning pansharpening methods. The codes are
available at https://github.com/Zeyu-Zhu/PGCU.Comment: 10 pages, 5 figure
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