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Deep learning approach for Sentinel-1 surface water mapping leveraging

Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud computing, the remote sensing scientific community is adapting modern deep learning approaches. The new integration of cloud-based Google AI platform and Google Earth Engine enables users to deploy calculations at scale. In this paper, we investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. The performance was then evaluated using an independent validation data set. We tested 12 models overall and found that the models utilizing the JRC data labels showed a better model performance, with F1-scores ranging from 0.972 to 0.986 for the training test and validation efforts. Additionally, an independently sampled high-resolution data set was used to further evaluate model performance. From this independent validation effort we observed models leveraging JRC data labels produced F1-Scores ranging from 0.9130.922. A pairwise comparison of models, through varying input data, learning rates, and loss functions constituents, revealed the JRC Adjusted Binary Cross Entropy Dice model to be statistically different than the 66 other model combinations and displayed the highest relative evaluations metrics including accuracy, precision score, Cohen Kappa coefficient, and F1-score. These results are in the same range as many of the conventional methods. We observed that the integration of Google AI Platform into Google Earth Engine can be a powerful tool to deploy deep-learning algorithms at scale and that automatic data labeling can be an effective strategy in the development of deep-learning models, however independent data validation remains an important step in model evaluation.

Availability
05621.3678Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
ISPRS Open Journal of Photogrammetry and Remote Sensing
Call Number
621.3678
Publisher
Amsteram : Elsevier.,
Collation
13 hlm PDF, 4.033 KB
Language
Inggris
ISBN/ISSN
1872-8235
Classification
621.3678
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.2, December 2021
Subject(s)
Image segmentation
Synthetic aperture radar
Surface water mapping
Deep learning
U-net
Google earth engine
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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  • Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine
    Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud computing, the remote sensing scientific community is adapting modern deep learning approaches. The new integration of cloud-based Google AI platform and Google Earth Engine enables users to deploy calculations at scale. In this paper, we investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. The performance was then evaluated using an independent validation data set. We tested 12 models overall and found that the models utilizing the JRC data labels showed a better model performance, with F1-scores ranging from 0.972 to 0.986 for the training test and validation efforts. Additionally, an independently sampled high-resolution data set was used to further evaluate model performance. From this independent validation effort we observed models leveraging JRC data labels produced F1-Scores ranging from 0.9130.922. A pairwise comparison of models, through varying input data, learning rates, and loss functions constituents, revealed the JRC Adjusted Binary Cross Entropy Dice model to be statistically different than the 66 other model combinations and displayed the highest relative evaluations metrics including accuracy, precision score, Cohen Kappa coefficient, and F1-score. These results are in the same range as many of the conventional methods. We observed that the integration of Google AI Platform into Google Earth Engine can be a powerful tool to deploy deep-learning algorithms at scale and that automatic data labeling can be an effective strategy in the development of deep-learning models, however independent data validation remains an important step in model evaluation.
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