Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data

Ducournau, Aurélien; Fablet, Ronan

doi:10.1109/prrs.2016.7867019

Cited by 75 publications

(44 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to other fields in geosciences, such as oceanography (e.g. Ducournau and Fablet, 2016;Lguensat et al, 2018), climatology (e.g. Rasp et al, 2018;Jiang et al, 2018) and hydrology (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In order to validate this approach, we use a case study with 32 French Alpine glaciers for which glacier-wide annual SMBs are available over the period 1984-2014 and 1959-2015 for certain glaciers. Highresolution meteorological reanalyses for the same time period are used (SAFRAN;Durand et al, 2009), while the initial ice thickness distribution of glaciers is taken from Farinotti et al (2019), for which we performed a sensitivity analysis based on field observations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning applied to glacier evolution modelling

et al. 2020

View full text Add to dashboard Cite

Abstract. We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; i.e. deep learning). This method has been included as the SMB component of an open-source regional glacier evolution model. While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated from topo-climatic predictors using either deep learning or Lasso (least absolute shrinkage and selection operator; regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French Alpine glaciers. Deep learning is found to outperform linear methods, with improved explained variance (up to +64 % in space and +108 % in time) and accuracy (up to +47 % in space and +58 % in time), resulting in an estimated r2 of 0.77 and a root-mean-square error (RMSE) of 0.51 m w.e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers in a whole region for past and future climates.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning applied to glacier evolution modelling

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In the context of the presented research, the idea is to find the value of the pixels in the images taken from higher altitudes and make them similar to those taken from a lower altitude. Recent works have considered super-resolution methods in remote sensing [40][41][42][43][44][45], satellite imagery [41][42][43][44][45][46][47][48][49][50][51], medicine [52][53][54][55], and microscopy [56][57][58][59].…”

Section: Introductionmentioning

confidence: 99%

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

Burdziakowski

2020

Remote Sensing

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) have now become very popular in photogrammetric and remote-sensing applications. Every day, these vehicles are used in new applications, new terrains, and new tasks, facing new problems. One of these problems is connected with flight altitude and the determined ground sample distance in a specific area, especially within cities and industrial and construction areas. The problem is that a safe flight altitude and camera parameters do not meet the required or demanded ground sampling distance or the geometrical and texture quality. In the cases where the flight level cannot be reduced and there is no technical ability to change the UAV camera or lens, the author proposes the use of a super-resolution algorithm for enhancing images acquired by UAVs and, consequently, increase the geometrical and interpretation quality of the final photogrammetric product. The main study objective was to utilize super-resolution (SR) algorithms to improve the geometric and interpretative quality of the final photogrammetric product, assess its impact on the accuracy of the photogrammetric processing and on the traditional digital photogrammetry workflow. The research concept assumes a comparative analysis of photogrammetric products obtained on the basis of data collected from small, commercial UAVs and products obtained from the same data but additionally processed by the super-resolution algorithm. As the study concludes, the photogrammetric products that are created as a result of the algorithms’ operation on high-altitude images show a comparable quality to the reference products from low altitudes and, in some cases, even improve their quality.

show abstract

“…There are various building blocks to construct a deep learning network for a specific task, among which convolutional neural network (CNN) turns to be an effective and efficient architecture for image feature extraction and recognition problems [25]. As the study goes on, CNN-based models are gradually applied in data fusion domain, and relevant research is being undertaken [13,23,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

Tan

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image fusion techniques provide a feasible and economical solution for generating dense-time data with high spatial resolution, pushing the limits of current satellite observation systems. Among existing various fusion algorithms, deeplearningbased models reveal a promising prospect with higher accuracy and robustness. This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. The contributions of this paper are twofold. First, the fusion result is improved considerably with brand-new network architecture and a novel compound loss function. Experiments conducted in two different areas demonstrate these improvements by comparing them with existing algorithms. The enhanced DCSTFN model shows superior performance with higher accuracy, vision quality, and robustness. Second, the advantages and disadvantages of existing deeplearningbased spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference for future research. Those comparisons and guidelines are summarized based on numbers of actual experiments and have promising potentials to be applied for other image sources with customized spatiotemporal fusion networks.

show abstract

Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data

Cited by 75 publications

References 16 publications

Deep learning applied to glacier evolution modelling

Deep learning applied to glacier evolution modelling

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

Contact Info

Product

Resources

About