52 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
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage 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
Exploring variability in medical imaging
Although recent successes of deep learning and novel machine learning techniques improved the perfor-
mance of classification and (anomaly) detection in computer vision problems, the application of these
methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this
is the amount of variability that is encountered and encapsulated in human anatomy and subsequently
reflected in medical images. This fundamental factor impacts most stages in modern medical imaging
processing pipelines.
Variability of human anatomy makes it virtually impossible to build large datasets for each disease
with labels and annotation for fully supervised machine learning. An efficient way to cope with this is
to try and learn only from normal samples. Such data is much easier to collect. A case study of such
an automatic anomaly detection system based on normative learning is presented in this work. We
present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative
models, which are trained only utilising normal/healthy subjects.
However, despite the significant improvement in automatic abnormality detection systems, clinical
routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis
and localise abnormalities. Integrating human expert knowledge into the medical imaging processing
pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per-
spective of building an automated medical imaging system, it is still an open issue, to what extent
this kind of variability and the resulting uncertainty are introduced during the training of a model
and how it affects the final performance of the task. Consequently, it is very important to explore the
effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as
on the model’s performance in a specific machine learning task. A thorough investigation of this issue
is presented in this work by leveraging automated estimates for machine learning model uncertainty,
inter-observer variability and segmentation task performance in lung CT scan images.
Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging
was attempted. This state-of-the-art survey includes both conventional pattern recognition methods
and deep learning based methods. It is one of the first literature surveys attempted in the specific
research area.Open Acces
Aplicações de modelos de deep learning para monitoramento ambiental e agrÃcola no Brasil
Tese (doutorado) — Universidade de BrasÃlia, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Algoritmos do novo campo de aprendizado de máquina conhecido como Deep Learning têm se popularizado recentemente, mostrando resultados superiores a modelos tradicionais em métodos de classificação e regressão. O histórico de sua utilização no campo
do sensoriamento remoto ainda é breve, porém eles têm mostrado resultados similarmente
superiores em processos como a classificação de uso e cobertura da terra e detecção de
mudança. Esta tese teve como objetivo o desenvolvimento de metodologias utilizando
estes algoritmos com um enfoque no monitoramento de alvos crÃticos no Brasil por via
de imagens de satélite a fim de buscar modelos de alta precisão e acurácia para substituir
metodologias utilizadas atualmente. Ao longo de seu desenvolvimento, foram produzidos
três artigos onde foi avaliado o uso destes algoritmos para a detecção de três alvos distintos:
(a) áreas queimadas no Cerrado brasileiro, (b) áreas desmatadas na região da Amazônia e
(c) plantios de arroz no sul do Brasil. Apesar do objetivo similar na produção dos artigos,
procurou-se distinguir suficientemente suas metodologias a fim de expandir o espaço metodológico conhecido para fornecer uma base teórica para facilitar e incentivar a adoção
destes algoritmos em contexto nacional. O primeiro artigo avaliou diferentes dimensões
de amostras para a classificação de áreas queimadas em imagens Landsat-8. O segundo
artigo avaliou a utilização de séries temporais binárias de imagens Landsat para a detecção
de novas áreas desmatadas entre os anos de 2017, 2018 e 2019. O último artigo utilizou
imagens de radar Sentinel-1 (SAR) em uma série temporal contÃnua para a delimitação dos
plantios de arroz no Rio Grande do Sul. Modelos similares foram utilizados em todos os
artigos, porém certos modelos foram exclusivos a cada publicação, produzindo diferentes
resultados. De maneira geral, os resultados encontrados mostram que algoritmos de Deep
Learning são não só viáveis para detecção destes alvos mas também oferecem desempenho superior a métodos existentes na literatura, representando uma alternativa altamente
eficiente para classificação e detecção de mudança dos alvos avaliados.Algorithms belonging to the new field of machine learning called Deep Learning have
been gaining popularity recently, showing superior results when compared to traditional
classification and regression methods. The history of their use in the field of remote sensing is not long, however they have been showing similarly superior results in processes
such as land use classification and change detection. This thesis had as its objective the
development of methodologies using these algorithms with a focus on monitoring critical
targets in Brazil through satellite imagery in order to find high accuracy and precision models to substitute methods used currently. Through the development of this thesis, articles
were produced evaluating their use for the detection of three distinct targets: (a) burnt
areas in the Brazilian Cerrado, (b) deforested areas in the Amazon region and (c) rice fields in the south of Brazil. Despite the similar objective in the production of these articles,
the methodologies in each of them was made sufficiently distinct in order to expand the
methodological space known. The first article evaluated the use of differently sized samples to classify burnt areas in Landsat-8 imagery. The second article evaluated the use of
binary Landsat time series to detect new deforested areas between the years of 2017, 2018
and 2019. The last article used continuous radar Sentinel-1 (SAR) time series to map rice
fields in the state of Rio Grande do Sul. Similar models were used in all articles, however
certain models were exclusive to each one. In general, the results show that not only are
the Deep Learning models viable but also offer better results in comparison to other existing methods, representing an efficient alternative when it comes to the classification and
change detection of the targets evaluated
Deep convolutional regression modelling for forest parameter retrieval
Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential.
Existing forest parameter estimation methods use regression models that establish pixel-wise relationships between ground reference data and corresponding pixels in remote sensing (RS) images. However, these models often overlook spatial contextual relationships among neighbouring pixels, limiting the potential for improved forest monitoring. The emergence of deep convolutional neural networks (CNNs) provides opportunities for enhanced forest parameter retrieval through their convolutional filters that allow for contextual modelling. However, utilising deep CNNs for regression presents its challenges. One significant challenge is that the training of CNNs typically requires continuous data layers for both predictor and response variables. While RS data is continuous, the ground reference data is sparse and scattered across large areas due to the challenges and costs associated with in situ data collection.
This thesis tackles challenges related to using CNNs for regression by introducing novel deep learning-based solutions across diverse forest types and parameters. To address the sparsity of available reference data, RS-derived prediction maps can be used as auxiliary data to train the CNN-based regression models. This is addressed through two different approaches.
Although these prediction maps offer greater spatial coverage than the original ground reference data, they do not ensure spatially continuous prediction target data. This work proposes a novel methodology that enables CNN-based regression models to handle this diversity. Efficient CNN architectures for the regression task are developed by investigating relevant learning objectives, including a new frequency-aware one. To enable large-scale and cost-effective regression modelling of forests, this thesis suggests utilising C-band synthetic aperture radar SAR data as regressor input. Results demonstrate the substantial potential of C-band SAR-based convolutional regression models for forest parameter retrieval
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