Enhancing dimensionality in remote sensing images

Remote sensing relies on diverse imaging modalities to capture critical information about the Earth’s surface. These modalities include panchromatic images, which are single-band grayscale representations; multispectral images, capturing a limited number of spectral bands; and hyperspectral images, encompassing a broad spectrum with numerous spectral bands. Images of the same scene are often acquired by different sensors, leading to datasets that are spatially registered but differ significantly in spatial and spectral resolutions. This variation in dimensionality presents challenges for image processing and analysis, necessitating techniques to enhance spatial, spectral, or combined spatial spectral resolutions. Dimensionality enhancement in remote sensing focuses on three primary tasks. Spatial enhancement improves the spatial resolution of images, such as through super-resolution techniques. Spectral enhancement increases spectral resolution or facilitates translation between spectral domains. Spatial-spectral enhancement combines high spatial and spectral resolutions from various sources, exemplified by pansharpening, where a high-resolution panchromatic image is fused with a low-resolution multispectral or hyperspectral image to produce a high-resolution output. These tasks are inherently complex due to the ill-posed nature of the underlying transformations. This dissertation addresses these challenges through a unified mathematical framework, employing operators to model the required transformations. However, directly solving these operator-based formulations is computationally prohibitive. As a solution, learning-based approaches are employed to reformulate the tasks as supervised or weakly supervised learning problems. These methods leverage data-driven models to approximate the mappings, offering practical and scalable solutions for dimensionality enhancement. This work introduces a unified framework addressing three critical problems in remote sensing: image super-resolution, image translation, and hyperspectral pansharpening. For each task, operator-based formulations define the mathematical principles, while learning-based formulations demonstrate the efficacy of data-driven methods. The proposed framework provides a comprehensive approach to improving spatial and spectral quality in remote sensing imagery, advancing the state-of-the-art in image enhancement techniques.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Electrical and Computer Engineering (UMKC)

Grantor dc:publisher

University of Missouri--Kansas City

Year dc:date.issued

2025

Author dc:creator

• Jiang, Hongcheng

Advisor dc:contributor.advisor

• Chen, ZhiQiang

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10355/109241

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/109241