8 research outputs found
Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure
The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural characteristics of the input data. Information-theoretic measures are often used to favor comparing local intensity distributions in the images. In this paper, a novel method based on the combination of a deep learning architecture and a correlation-type area-based functional is proposed for the registration of a multisensor pair of images, including an optical image and a synthetic aperture radar (SAR) image. The method makes use of a conditional generative adversarial network (cGAN) in order to address image-to-image translation across the optical and SAR data sources. Then, once the optical and SAR data are brought to a common domain, an area-based ℓ2 similarity measure is used together with the COBYLA constrained maximization algorithm for registration purposes. While correlation-type functionals are usually ineffective in the application to multisensor registration, exploiting the image-to-image translation capabilities of cGAN architectures allows moving the complexity of the comparison to the domain adaptation step, thus enabling the use of a simple ℓ2 similarity measure, favoring high computational efficiency, and opening the possibility to process a large amount of data at runtime. Experiments with multispectral and panchromatic optical data combined with SAR images suggest the effectiveness of this strategy and the capability of the proposed method to achieve more accurate registration as compared to state-of-the-art approaches
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
In this paper, we consider the problem of change detection (CD) with two
heterogeneous remote sensing (RS) images. For this problem, an unsupervised
change detection method has been proposed recently based on the image
translation technique of Cycle-Consistent Adversarial Networks (CycleGANs),
where one image is translated from its original modality to the modality of the
other image so that the difference map can be obtained by performing
arithmetical subtraction. However, the difference map derived from subtraction
is susceptible to image translation errors, in which case the changed area and
the unchanged area are less distinguishable. To overcome the above shortcoming,
we propose a new unsupervised copula mixture and CycleGAN-based CD method
(COMIC), which combines the advantages of copula mixtures on statistical
modeling and the advantages of CycleGANs on data mining. In COMIC, the
pre-event image is first translated from its original modality to the
post-event image modality. After that, by constructing a copula mixture, the
joint distribution of the features from the heterogeneous images can be learnt
according to quantitive analysis of the dependence structure based on the
translated image and the original pre-event image, which are of the same
modality and contain totally the same objects. Then, we model the CD problem as
a binary hypothesis testing problem and derive its test statistics based on the
constructed copula mixture. Finally, the difference map can be obtained from
the test statistics and the binary change map (BCM) is generated by K-means
clustering. We perform experiments on real RS datasets, which demonstrate the
superiority of COMIC over the state-of-the-art methods
Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with the state-of-the-art algorithms. Experiments conducted on three real data sets show the effectiveness of our methodology
Deep learning-based change detection in remote sensing images:a review
Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.</jats:p
Aplicações de modelos de deep learning para monitoramento ambiental e agrícola no Brasil
Tese (doutorado) — Universidade de Brasília, 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 críticos 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 contínua 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