Simultaneous classification and location of volcanic deformation in SAR interferograms using deep learning and the VolcNet database

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.


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Matthew Gaddes, Andy Hooper , Fabien Albino 


With the evolution of InSAR into a tool for active hazard monitoring, through its ability to detect ground deformation with low latency, new methods are sought to quickly and automatically interpret the large number of interferograms that are created. In this work, we present a convolutional neural network (CNN) that is able to both classify the type of deformation, and to locate the deformation within an interferogram in a single step. We achieve this through building a ``two headed model", which is able to return both outputs after one forward pass of an interferogram though the network, and so does not require the use of a sliding window approach for localisation. We train our model by first creating a large dataset of synthetic interferograms which feature labels of both the type and location of any deformation, and we release the Python3 code for this as a package named SyInterferoPy. We find that our model's performance is improved through the inclusion of just a small amount of augmented real Sentinel-1 data, and retrain our model accordingly. We also release this set of labelled training data as a database named VolcNet. When building models of this type, it is common for some of the weights within the model to be transferred from other models designed for different problems. Consequently, we also investigate how to best organise interferograms such that the filters learned in models such as VGG16 are sensitive to the signals of interest in interferograms, but find that using different data in each of the three input channels significantly degrades performance when compared to the simple case of repeating wrapped and unwrapped phase across each channel. This implies that the inclusion of supplementary data, which we expect should improve the ability to distinguish deformation from noise, requires training of a network from scratch.





machine learning, Deep learning, InSAR, Sentinel-1, convolutional neural network


Published: 2021-01-12 15:50

Last Updated: 2021-03-22 10:33

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CC BY Attribution 4.0 International

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