Advanced modelling and analytics for effective change and anomaly detection in hyperspectral images.
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. Experimental results demonstrate that these two proposed techniques outperform a few state-of-the-art methods on several commonly used hyperspectral change detection datasets, highlighting their superiority in practical applications. Moreover, this thesis introduces a novel deep learning-based model called GASSM, marking the first exploration of combining the state-space-model (SSM) based Mamba model with the global attention for hyperspectral change detection. GASSM effectively overcomes the limitations of traditional convolutional neural networks in terms of the limited receptive field and the high computational complexity associated with transformer-based methods, offering new directions for future research. Additionally, this study proposes a background reconstruction-based hyperspectral anomaly detection method, which has been shown to exhibit robustness and high detection accuracy across six different scenario datasets. Overall, this study significantly advances the field of hyperspectral change detection and anomaly detection by proposing and validating several novel models and analytics methods, laying a solid foundation for further research and applications in this area.
Hyperspectral imaging, Hyperspectral change detection, Hyperspectral anomaly detection, Computer vision, Deep learning
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