当前位置: X-MOL 学术J. Appl. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Optimizing deep learning model selection for angular feature extraction in satellite imagery
Journal of Applied Remote Sensing ( IF 1.7 ) Pub Date : 2020-06-08 , DOI: 10.1117/1.jrs.14.032612
Poppy G. Immel 1 , Meera A. Desai 1 , Daniela I. Moody 1
Affiliation  

Abstract. Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of convolutional neural network (CNN) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine learning problem that can be applied to nontraditional real-world feature extraction and automation tasks. Ursa Space Systems Inc. develops machine learning approaches to build custom solutions and extract answers from synthetic aperture radar satellite data fused with other remote sensing data sets. One application is identifying the orientation of nontexture linear features in imagery, such as an inlet pipe on top of a cylindrical oil storage tank. We propose a two-phase approach for determining this orientation: first an optimized CNN is used in a nontraditional way to probabilistically determine a coarse location and orientation of the inlet pipe, followed by a maximum likelihood voting scheme to automatically extract the orientation of the angular feature within 7.5 deg. We use a known hyperparameter optimization technique to determine the best deep learning CNN architecture for our specific problem and under user-defined optimization and accuracy constraints, by optimizing model hyperparameters (number of layers, size of the input image, and data set preprocessing) using a manual and grid search approach. The use of this systematic approach for hyperparameter optimization yields increased accuracy for our angular feature extraction and orientation finding algorithm from 86% to 94%. Additionally, this proposed algorithm shows how machine learning can be used to improve real-world remote sensing workflows.

中文翻译:

卫星图像角度特征提取的深度学习模型选择优化

摘要。在过去的几十年中,深度学习技术已被用于众多应用程序和不同的数据模式,最近在遥感图像领域。鉴于卷积神经网络 (CNN) 架构的复杂性和深度,很难全面评估性能、优化超参数并为特定机器学习问题提供稳健的解决方案,这些解决方案可应用于非传统的现实世界特征提取和自动化任务. Ursa Space Systems Inc. 开发机器学习方法来构建定制解决方案,并从与其他遥感数据集融合的合成孔径雷达卫星数据中提取答案。一种应用是识别图像中非纹理线性特征的方向,如圆柱形储油罐顶部的进油管。我们提出了一种确定该方向的两阶段方法:首先以非传统方式使用优化的 CNN 来概率性地确定入口管的粗略位置和方向,然后是最大似然投票方案以自动提取角的方向7.5 度以内的特征。我们使用已知的超参数优化技术,通过优化模型超参数(层数、输入图像的大小和数据集预处理),为我们的特定问题确定最佳深度学习 CNN 架构,并在用户定义的优化和精度约束下使用手动和网格搜索方法。使用这种系统方法进行超参数优化可以将我们的角度特征提取和方向查找算法的准确度从 86% 提高到 94%。此外,这个提议的算法展示了如何使用机器学习来改进现实世界的遥感工作流程。
更新日期:2020-06-08
down
wechat
bug