Image Processing via Multilayer Graph Spectra

Zhang, Songyang; Deng, Qinwen; Ding, Zhi

doi:10.48550/arxiv.2108.13639

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…Extending the M-GSP introduced in this work, we have successfully develop additional M-GSP applications in image processing, smart health, and hyperspectral image analysis. Interested readers may refer to [53], [54] for more details.…”

Section: E Other Potential Applications In Iot Systemsmentioning

confidence: 99%

Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications

Zhang

Deng

Ding

2024

IEEE Internet Things J.

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Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many reallife datasets and systems, including those in Internet of Things (IoT), are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are harder to be captured with a single-layer graph, and can be better characterized by multilayers graph connections. Such multilayer or multi-level data structures can be more naturally modeled by high-dimensional multilayer graphs (MLG). To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP). Specifically, we introduce core concepts of M-GSP and study properties of MLG spectral space, followed by fundamentals of MLG-based filter design. To illustrate novel aspects of M-GSP, we further explore its link with traditional signal processing and GSP. We provide example applications to demonstrate the efficacy and benefits of applying multilayer graphs and M-GSP in practical scenarios.

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Section: E Other Potential Applications In Iot Systemsmentioning

confidence: 99%

Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications

Zhang

Deng

Ding

2024

IEEE Internet Things J.

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“…Here, we mainly focus on fundamentals of singular analysis of the undirected multilayer networks. For more details on other concepts, such as MLN Fourier transform, M-GSP filter design, sampling theory and stationary process, readers are referred to [35], [36].…”

Section: Fundamentals Of M-gspmentioning

confidence: 99%

“…In view of the aforementioned challenges, this work introduces a new approach of graph signal processing over multilayer networks (M-GSP) to HSI processing. M-GSP [35], [36] is a tensor-based framework that generalizes traditional graph signal processing (GSP) [37] to process the heterogeneous graph structures across different layers of graphs. Since the spatial positions (pixels) are the same for all hyperspectral bands, we can model HSI data as a multilayer network with the same number of nodes in each layer (also known as multiplex network).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks

Zhang

Deng

Ding

2021

Preprint

Self Cite

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Hyperspectral imaging is an important sensing technology with broad applications and impact in areas including environmental science, weather, and geo/space exploration. One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. Leveraging on the recentdeveloped graph signal processing over multilayer networks (M-GSP), this work proposes several approaches to HSI segmentation based on M-GSP feature extraction. To capture joint spectralspatial information, we first customize a tensor-based multilayer network (MLN) model for HSI, and define a MLN singular space for feature extraction. We then develop an unsupervised HSI segmentation method by utilizing MLN spectral clustering. Regrouping HSI pixels via MLN-based clustering, we further propose a semi-supervised HSI classification based on multi-resolution fusions of superpixels. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.

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Image Processing via Multilayer Graph Spectra

Cited by 2 publications

References 33 publications

Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications

Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications

Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks

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