Work

Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

Year: 2019

Type: conference-paper

Abstract: Urban material recognition in remote sensing imagery is a challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation-based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.

Source: IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

DOI: 10.1109/igarss.2019.8900639

Authors: Arthita Ghosh, Max Ehrlich, Larry Davis, Rama Chellappa

Institution: University of Maryland, College Park

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2983782757

FWCI: 0.793

Cites: 26

Cited by: 3

Related to: 10