Ciotola, Matteo (2023) Deep Learning-based Pansharpening and Super-Resolution of Remote Sensing Images. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Deep Learning-based Pansharpening and Super-Resolution of Remote Sensing Images |
| Creators: | Creators Email Ciotola, Matteo matteo.ciotola@unina.it |
| Date: | 11 December 2023 |
| Number of Pages: | 270 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Russo, Stefano stefano.russo@unina.it |
| Tutor: | nome email Poggi, Giovanni UNSPECIFIED Scarpa, Giuseppe UNSPECIFIED |
| Date: | 11 December 2023 |
| Number of Pages: | 270 |
| Keywords: | Remote sensing, Image processing, Data fusion, Pansharpening |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 - Telecomunicazioni |
| Date Deposited: | 11 Dec 2023 19:11 |
| Last Modified: | 05 May 2026 07:57 |
| URI: | http://www.fedoa.unina.it/id/eprint/15666 |
Collection description
Satellite remote sensing provides detailed, large-scale Earth images. Many applications rely on this information, and there is a strong demand for more and better data. No single sensor provides all the information of interest, which motivates the growing appeal of data fusion. Due to the limitations of the sensors, the acquired images cannot have simultaneously high spatial and spectral resolution. To overcome this problem, two coupled sensors can be used, acquiring a high-resolution panchromatic image and a low-resolution multispectral image. The fusion technique known as \textit{pansharpening} aims to fuse them to obtain an ideal high-resolution multispectral image. Many model-based pansharpening methods have been developed in recent decades. Recently, research has shifted towards data-driven solutions, hoping to replicate the successes observed in other application fields. The results, however, did not meet these high expectations. This is likely due to the lack of full-resolution real-world data, which prevents the use of supervised learning. To around this limitation, many models are trained on low-resolution synthetic data and then used on the high-resolution data of interest. This approach, however, is based on a dubious assumption of scale invariance and provides questionable results. This thesis proposes a new training framework that works on original high-resolution images, avoiding downscaling and consequent impairments. The framework encompasses novel methods to evaluate the spectral and spatial fidelity of the pansharpened image compared to the original multispectral and panchromatic data. Experiments on real data demonstrate that the proposed methods outperform the current state of the art. Further contributions include on-the-fly band co-registration and weights adaptation, and new perception-based distortion indexes.
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