Cross-modality learning for earth surface mapping with cloud-covered satellite optical images and radar images
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...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/151026 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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