269 research outputs found
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends
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
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Open set learning with augmented category by exploiting unlabelled data (open-LACU)
Considering the nature of unlabelled data, it is common for partially
labelled training datasets to contain samples that belong to novel categories.
Although these so-called observed novel categories exist in the training data,
they do not belong to any of the training labels. In contrast, open-sets define
novel categories as those unobserved during during training, but present during
testing. This research is the first to generalize between observed and
unobserved novel categories within a new learning policy called open-set
learning with augmented category by exploiting unlabeled data or open-LACU.
This study conducts a high-level review on novelty detection so to
differentiate between research fields that concern observed novel categories,
and the research fields that concern unobserved novel categories. Open-LACU is
then introduced as a synthesis of the relevant fields to maintain the
advantages of each within a single learning policy. Currently, we are
finalising the first open-LACU network which will be combined with this
pre-print to be sent for publication.Comment: 11 Page
The Role of Synthetic Data in Improving Supervised Learning Methods: The Case of Land Use/Land Cover Classification
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information ManagementIn remote sensing, Land Use/Land Cover (LULC) maps constitute important assets for
various applications, promoting environmental sustainability and good resource management.
Although, their production continues to be a challenging task. There are various factors
that contribute towards the difficulty of generating accurate, timely updated LULC maps,
both via automatic or photo-interpreted LULC mapping. Data preprocessing, being a
crucial step for any Machine Learning task, is particularly important in the remote sensing
domain due to the overwhelming amount of raw, unlabeled data continuously gathered
from multiple remote sensing missions. However a significant part of the state-of-the-art
focuses on scenarios with full access to labeled training data with relatively balanced class
distributions. This thesis focuses on the challenges found in automatic LULC classification
tasks, specifically in data preprocessing tasks. We focus on the development of novel
Active Learning (AL) and imbalanced learning techniques, to improve ML performance in
situations with limited training data and/or the existence of rare classes. We also show
that much of the contributions presented are not only successful in remote sensing problems,
but also in various other multidisciplinary classification problems. The work presented
in this thesis used open access datasets to test the contributions made in imbalanced
learning and AL. All the data pulling, preprocessing and experiments are made available at
https://github.com/joaopfonseca/publications. The algorithmic implementations are made
available in the Python package ml-research at https://github.com/joaopfonseca/ml-research
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