A Reproducible and Reusable Pipeline for Segmentation of Geoscientific Imagery
A Reproducible and Reusable Pipeline for Segmentation of Geoscientific Imagery
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DOI：
10.1029/2022ea002332
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发表时间：
2022-09-01
影响因子：
3.1
通讯作者：
Goldstein, E. B.
中科院分区：
文献类型：
--
作者：
Buscombe, D.;Goldstein, E. B.
地球科学图像分割是一项越来越普遍的任务。在使用深度学习的现代技术中，UNet架构已被证明是分割一系列图像的可靠方法。我们开发的软件-分割体操，实现一个数据模型管道分割的科学图像使用一个家庭的UNet模型。对于现有的一组图像和标签，该软件使用单个配置文件来处理数据集创建以及模型设置和模型训练。该软件的主要优点是（a）专注于可再现的数据集创建和建模，以及（B）通过更改配置文件进行快速模型实验的能力。快速实验允许研究人员对不同的模型架构、大小进行原型设计，并调整常见的超参数以找到合适的模型。我们使用419标记Landsat-8的沿海环境场景的数据集，并比较两个模型架构，五个模型大小和三个损失函数的结果，演示了该软件的使用。该演示突出表明，我们的软件能够快速、可重复地进行实验，以确定特定数据集和研究问题的最佳超参数。简明语言摘要地球科学家的一项常见任务是将卫星或航空图像划分为特定类别。例如，海岸线的图像可以被指定为水、海滩和陆地的某些像素。在深度学习世界中，这被称为分割。我们编写了一个软件，帮助研究人员训练深度学习模型对所有类型的图像进行分割。制作深度学习模型的一个主要问题是处理使用哪个模型的所有选择，并快速测试许多选项。我们已经设计了我们的代码，它可以很容易地调整，并将在许多应用程序和许多常见类型的地球科学图像数据集的工作。
Segmentation of Earth science imagery is an increasingly common task. Among modern techniques that use Deep Learning, the UNet architecture has been shown to be a reliable for segmenting a range of imagery. We developed software-Segmentation Gym-to implement a data-model pipeline for segmentation of scientific imagery using a family of UNet models. With an existing set of imagery and labels, the software uses a single configuration file that handles data set creation, as well as model setup and model training. Key benefits of this software are (a) the focus on reproducible data set creation and modeling, and (b) the ability for quick model experimentation through changes to a configuration file. Quick experimentation permits researchers to prototype different model architectures, sizes, and adjust common hyperparameters to find a suitable model. We demonstrate the use of the software using a data set of 419 labeled Landsat-8 scenes of coastal environments and compare results across two model architectures, five model sizes, and three loss functions. This demonstration highlights that our software enables rapid, reproducible experimentation to determine optimal hyperparameters for specific data sets and research questions.Plain Language Summary A common task for Earth scientists is to divide a satellite or aerial image into specific classes. For example, an image of the coastline might be assigned certain pixels as being water, beach, and land. In the Deep Learning world, this is called segmentation. We wrote a piece of software that helps researchers train Deep Learning models to do segmentation on all types of imagery. A major problem with making Deep Learning models is dealing with all the choices on which model to use and quickly testing many options. We have designed our code in such a way that it can easily be adjusted, and will work in many applications and for many common types of Earth science image data sets.