Adversarial Learning Based Discriminative Domain Adaptation for Geospatial Image Analysis
The ability of supervised image analysis methods to provide state-of-the-art performance is limited by availability of high-quality labeled data in large quantities. Domain adaptation approaches propose a solution to this problem by leveraging quality labeled information from auxiliary data sources. In this work, we use adversarial learning for domain adaptation for remote sensing applications. First, we approached the problem of unavailable target domain labels with unsupervised domain adaptation and then extended our method for semisupervised domain adaptation to use a few available labels as well. We are using adversarial learning to extract discriminative target domain features that are aligned with source domain. We test our framework for two very different applications of remote sensing imagery, multiclass classification in hyperspectral images and semantic segmentation in large scale satellite images. For hyperspectral image analysis two datasets were used: the University of Houston shadow data was used for quantifying the efficacy of our approach to varying illumination, and the Botswana data was used to quantify the efficacy of our approach under multitemporal spectral shifts. Multisensor high-resolution images from National Agriculture Imagery Program and SpaceNet-Rio datasets were used as the source and target for the task of building extraction for large scale semantic segmentation based domain adaptation.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1833756
- Alternate ID(s):
- OSTI ID: 1833757; OSTI ID: 1838988
- Journal Information:
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Name: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 15; ISSN 1939-1404
- Publisher:
- Institute of Electrical and Electronics EngineersCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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