Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

Mou, Lichao; Zhu, Xiao Xiang; Vakalopoulou, Maria; Καράντζαλος, Κωνσταντίνος; Paragios, Nikos; Saux, Bertrand Le; Moser, Gabriele; Tuia, Devis

doi:10.1109/jstars.2017.2696823

Cited by 67 publications

(47 citation statements)

References 56 publications

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“…Data fusion is one of the fast-moving areas of remote sensing [114][115][116]: due to the recent increases in availability of sensor data, the perspectives of using big and heterogeneous data to study environmental processes have become more tangible. It is a special instance of the more general problem of super-resolution.…”

Section: Multimodal Data Fusionmentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,442

1,204

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.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. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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Section: Multimodal Data Fusionmentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,442

1,204

View full text Add to dashboard Cite

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“…More precisely, it aims at designing new LCZ classification solutions based on open data, both from remote sensing and GIS, with particular attention to the issue of generalizing results to new urban areas unseen during model training. It follows a tradition of yearly data processing competitions [21]- [30] organized by the Image Analysis and Data Fusion Technical Committee (IADF TC 3 ) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS): Every year since 2006, a dataset has been released to the scientific community and participants have been invited to perform a task of interest, which, in the case of the DFC17 was LCZ classification over several cities using open, global, and multimodal data.…”

Section: Introductionmentioning

confidence: 99%

Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

Yokoya

Ghamisi

Xia

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

123

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In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.

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“…While most pixels belonging to the category are identified correctly, they are not correctly separated into instances (see arrows in the lower left image). small and cheap commercial high-resolution satellites and the now widespread availability of unmanned aerial vehicles (UAVs) -which facilitates a diversity of applications, such as urban management [1][2][3][4], monitoring of land changes [5][6][7][8], and traffic monitoring [9,10]. Among these applications, object extraction from very high-resolution remote sensing images/videos has gained increasing attention in the remote sensing community in recent years, particularly vehicle extraction, due to successful civil applications.…”

Section: Introductionmentioning

confidence: 99%

“…Regions in the image satisfy r k ∩ rt = ∅, ∀k = t and ∪r k = Ω, in where Ω is the whole image region 2. Semantic boundary detection is to detect the boundaries of each object instance in the images.…”

mentioning

confidence: 99%

Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network

Mou

Zhu

2018

IEEE Trans. Geosci. Remote Sensing

182

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This is a preprint, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE XPlore.Object detection and semantic segmentation are two main themes in object retrieval from high-resolution remote sensing images, which have recently achieved remarkable performance by surfing the wave of deep learning and, more notably, convolutional neural networks (CNNs). In this paper, we are interested in a novel, more challenging problem of vehicle instance segmentation, which entails identifying, at a pixel-level, where the vehicles appear as well as associating each pixel with a physical instance of a vehicle. In contrast, vehicle detection and semantic segmentation each only concern one of the two. We propose to tackle this problem with a semantic boundary-aware multi-task learning network. More specifically, we utilize the philosophy of residual learning (ResNet) to construct a fully convolutional network that is capable of harnessing multi-level contextual feature representations learned from different residual blocks. We theoretically analyze and discuss why residual networks can produce better probability maps for pixel-wise segmentation tasks. Then, based on this network architecture, we propose a unified multi-task learning network that can simultaneously learn two complementary tasks -namely, segmenting vehicle regions and detecting semantic boundaries. The latter subproblem is helpful for differentiating "touching" vehicles, which are usually not correctly separated into instances. Currently, datasets with pixel-wise annotation for vehicle extraction are ISPRS dataset and IEEE GRSS DFC2015 dataset over Zeebrugge, which specializes in semantic segmentation. Therefore, we built a new, more challenging dataset for vehicle instance segmentation, called the Busy Parking Lot UAV Video dataset, and we make our dataset available at http://www.sipeo.bgu.tum.de/downloads so that it can be used to benchmark future vehicle instance segmentation algorithms.

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Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

Cited by 67 publications

References 56 publications

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network

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