Data Fusion and Remote Sensing: An ever-growing relationship

Schmitt, Michael; Zhu, Xiao Xiang

doi:10.1109/mgrs.2016.2561021

Cited by 261 publications

(152 citation statements)

References 151 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.

Self Cite

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.

Self Cite

2,442

1,204

View full text Add to dashboard Cite

show abstract

“…Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly considering the corresponding labels of 3D space where points located, which can provide benchmark datasets of various sensors for the validation and evaluation of algorithms related to point cloud interpretation. Moreover, our proposed voting based approach for assignging labels to the 3D space can also be used for the purpose of fusing datasets of multiple sensors, which is also a promising research topic of the community of remote sensing (Schmitt and Zhu, 2016).…”

Section: Resultsmentioning

confidence: 99%

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

Sun

Boerner

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line-and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

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“…As mentioned in Section 1, a feasible solution for the improvement of TanDEM-X elevation data over urban areas is the fusion with Cartosat-1 DEM data, which were shown to provide much better accuracy in urban areas. Various methods have been formulated for DEM fusion (Crosetto et al 1998;Reinartz et al 2005;Papasaika et al 2011;Kuschk et al 2016;Fuss et al 2016), an important application case of data fusion in the field of remote sensing (Schmitt and Zhu, 2016). Among the aforementioned methods, the simplest and most popular one is weighted averaging (WA) (Reinartz et al, 2005).…”

Section: Dem Fusion Over Urban Areasmentioning

confidence: 99%

Uncertainty Assessment and Weight Map Generation for Efficient Fusion of Tandem-X and Cartosat-1 Dems

Bagheri

Schmitt

Zhu

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently, with InSAR data provided by the German TanDEM-X mission, a new global, high-resolution Digital Elevation Model (DEM) has been produced by the German Aerospace Center (DLR) with unprecedented height accuracy. However, due to SAR-inherent sensor specifics, its quality decreases over urban areas, making additional improvement necessary. On the other hand, DEMs derived from optical remote sensing imagery, such as Cartosat-1 data, have an apparently greater resolution in urban areas, making their fusion with TanDEM-X elevation data a promising perspective. The objective of this paper is two-fold: First, the height accuracies of TanDEM-X and Cartosat-1 elevation data over different land types are empirically evaluated in order to analyze the potential of TanDEM-XCartosat-1 DEM data fusion. After the quality assessment, urban DEM fusion using weighted averaging is investigated. In this experiment, both weight maps derived from the height error maps delivered with the DEM data, as well as more sophisticated weight maps predicted by a procedure based on artificial neural networks (ANNs) are compared. The ANN framework employs several features that can describe the height residual performance to predict the weights used in the subsequent fusion step. The results demonstrate that especially the ANN-based framework is able to improve the quality of the final DEM through data fusion.

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Data Fusion and Remote Sensing: An ever-growing relationship

Cited by 261 publications

References 151 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

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

Uncertainty Assessment and Weight Map Generation for Efficient Fusion of Tandem-X and Cartosat-1 Dems

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