47 research outputs found
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
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鈥搒pectral 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
Techniques for the extraction of spatial and spectral information in the supervised classification of hyperspectral imagery for land-cover applications
The objective of this PhD thesis is the development of spatialspectral
information extraction techniques for supervised
classification tasks, both by means of classical models and
those based on deep learning, to be used in the classification
of land use or land cover (LULC) multi- and hyper-spectral
images obtained by remote sensing. The main goal is the
efficient application of these techniques, so that they are able
to obtain satisfactory classification results with a low use of
computational resources and low execution time
A generic Self-Supervised Learning (SSL) framework for representation learning from spectral鈥搒patial features of unlabeled remote sensing imagery
Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data for model training, which is costly and time-consuming. The recent introduction of self-supervised learning (SSL) enables models to learn a representation from orders of magnitude more unlabeled data. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabeled data. Since remote sensing imagery has rich spectral information beyond the standard RGB color space, it may not be straightforward to extend to the multi/hyperspectral domain the pretext tasks established in computer vision based on RGB images. To address this challenge, this work proposed a generic self-supervised learning framework based on remote sensing data at both the object and pixel levels. The method contains two novel pretext tasks, one for object-based and one for pixel-based remote sensing data analysis methods. One pretext task is used to reconstruct the spectral profile from the masked data, which can be used to extract a representation of pixel information and improve the performance of downstream tasks associated with pixel-based analysis. The second pretext task is used to identify objects from multiple views of the same object in multispectral data, which can be used to extract a representation and improve the performance of downstream tasks associated with object-based analysis. The results of two typical downstream task evaluation exercises (a multilabel land cover classification task on Sentinel-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets) demonstrate that the proposed SSL method learns a target representation that covers both spatial and spectral information from massive unlabeled data. A comparison with currently available SSL methods shows that the proposed method, which emphasizes both spectral and spatial features, outperforms existing SSL methods on multi- and hyperspectral remote sensing datasets. We believe that this approach has the potential to be effective in a wider range of remote sensing applications and we will explore its utility in more remote sensing applications in the future
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery
Remote sensing data has been widely used for various Earth Observation (EO)
missions such as land use and cover classification, weather forecasting,
agricultural management, and environmental monitoring. Most existing remote
sensing data-based models are based on supervised learning that requires large
and representative human-labelled data for model training, which is costly and
time-consuming. Recently, self-supervised learning (SSL) enables the models to
learn a representation from orders of magnitude more unlabelled data. This
representation has been proven to boost the performance of downstream tasks and
has potential for remote sensing applications. The success of SSL is heavily
dependent on a pre-designed pretext task, which introduces an inductive bias
into the model from a large amount of unlabelled data. Since remote sensing
imagery has rich spectral information beyond the standard RGB colour space, the
pretext tasks established in computer vision based on RGB images may not be
straightforward to be extended to the multi/hyperspectral domain. To address
this challenge, this work has designed a novel SSL framework that is capable of
learning representation from both spectra-spatial information of unlabelled
data. The framework contains two novel pretext tasks for object-based and
pixel-based remote sensing data analysis methods, respectively. Through two
typical downstream tasks evaluation (a multi-label land cover classification
task on Sentienl-2 multispectral datasets and a ground soil parameter retrieval
task on hyperspectral datasets), the results demonstrate that the
representation obtained through the proposed SSL achieved a significant
improvement in model performance
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Spectral pixels are often a mixture of the pure spectra of the materials,
called endmembers, due to the low spatial resolution of hyperspectral sensors,
double scattering, and intimate mixtures of materials in the scenes. Unmixing
estimates the fractional abundances of the endmembers within the pixel.
Depending on the prior knowledge of endmembers, linear unmixing can be divided
into three main groups: supervised, semi-supervised, and unsupervised (blind)
linear unmixing. Advances in Image processing and machine learning
substantially affected unmixing. This paper provides an overview of advanced
and conventional unmixing approaches. Additionally, we draw a critical
comparison between advanced and conventional techniques from the three
categories. We compare the performance of the unmixing techniques on three
simulated and two real datasets. The experimental results reveal the advantages
of different unmixing categories for different unmixing scenarios. Moreover, we
provide an open-source Python-based package available at
https://github.com/BehnoodRasti/HySUPP to reproduce the results