Advanced modelling and analytics for effective change and anomaly detection in hyperspectral images.
type: CreativeWork
description: The main objective of this research is to design and implement novel models and analytics techniques for hyperspectral change detection and anomaly detection. With the widespread applications of hyperspectral imagery (HSI) in fields such as remote sensing, environmental monitoring and agriculture, the need for accurate and efficient change detection and anomaly detection has become increasingly critical. However, existing methods often face huge challenges related to the complexity of processing high-dimensional HSI data, especially the severe sensitivity to noise that causes low detection accuracy, and high computational costs. To address these issues, this thesis first provides a comprehensive literature review of the current state of research in hyperspectral change detection and anomaly detection, systematically organising the representative algorithms and analysing their trends and advancements in the past, especially in the recent three years. Building on this foundation, the thesis proposes a novel accumulated band-wise binary distancing (ABBD) model for unsupervised parameter-free HCD, which requires no parameter setting and can maintain high detection accuracy across different scenarios, thereby simplifying the operational complexity in practical applications. Additionally, this study introduces a novel 2D self-attention module, leading to the development of two lightweight deep learning networks focused on extracting local spatial-spectral features for more accurate change detection. The first network, namely CBANet, integrates a cross-band feature extraction module with the 2D self-attention, achieving higher detection accuracy and fewer hyperparameters compared to other advanced deep learning-based methods. The second lightweighted network, SSA-LHCD, combines the singular spectrum analysis (SSA) as a preprocessing step with a 2D self-attention module, further improving the detection accuracy while reducing the number of the hyperparameters of the model. …
abstract: The main objective of this research is to design and implement novel models and analytics techniques for hyperspectral change detection and anomaly detection. With the widespread applications of hyperspectral imagery (HSI) in fields such as remote sensing, environmental monitoring and agriculture, the need for accurate and efficient change detection and anomaly detection has become increasingly critical. However, existing methods often face huge challenges related to the complexity of processing high-dimensional HSI data, especially the severe sensitivity to noise that causes low detection accuracy, and high computational costs. To address these issues, this thesis first provides a comprehensive literature review of the current state of research in hyperspectral change detection and anomaly detection, systematically organising the representative algorithms and analysing their trends and advancements in the past, especially in the recent three years. Building on this foundation, the thesis proposes a novel accumulated band-wise binary distancing (ABBD) model for unsupervised parameter-free HCD, which requires no parameter setting and can maintain high detection accuracy across different scenarios, thereby simplifying the operational complexity in practical applications. Additionally, this study introduces a novel 2D self-attention module, leading to the development of two lightweight deep learning networks focused on extracting local spatial-spectral features for more accurate change detection. The first network, namely CBANet, integrates a cross-band feature extraction module with the 2D self-attention, achieving higher detection accuracy and fewer hyperparameters compared to other advanced deep learning-based methods. The second lightweighted network, SSA-LHCD, combines the singular spectrum analysis (SSA) as a preprocessing step with a 2D self-attention module, further improving the detection accuracy while reducing the number of the hyperparameters of the model. …
author: Li, Yinhe
language: en
keywords: Hyperspectral imaging, Hyperspectral change detection, Hyperspectral anomaly detection, Computer vision, Deep learning
date published: 2024
date modified: 2026-07-24T04:10:09Z
provider: Robert Gordon University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Robert Gordon University

PropertyValue
property ID: DOI
value: 10.48526/rgu-wt-2795662