Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151026
Title: Cross-modality learning for earth surface mapping with cloud-covered satellite optical images and radar images
Authors: Pi, Ziyi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Pi, Z. (2021). Cross-modality learning for earth surface mapping with cloud-covered satellite optical images and radar images. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151026
Project: A3153-201
Abstract: There are two common kinds of images used in land classification and recognition in remote sensing technology: optical images and polarimetric synthetic aperture radar (PolSAR) images. However, optical images can be covered by clouds for a long time due to weather problems, which is one of the most serious challenges for remote sensing technology. Therefore, the purpose of the project is to recover the landscape for cloud-covered areas on optical images, based on the reference from PolSAR images. In the report, the author includes different kinds of approaches to recover the landscape, including three following methods to derive the optimal method: Direct Method: Different kinds of searching or similarity algorithms. Poisson Method: Seamless cloning or mixing gradients. Pan Sharpening Method: Combination of Poisson and Pan Sharpening.
URI: https://hdl.handle.net/10356/151026
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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