Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image
- URL: http://arxiv.org/abs/2104.11904v1
- Date: Sat, 24 Apr 2021 08:09:27 GMT
- Title: Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image
- Authors: Qi Wang, Yanling Miao, Mulin Chen, Xuelong Li
- Abstract summary: This paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering.
The proposed SSCAG is competitive against the state-of-the-art approaches.
- Score: 88.60285937702304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral
pixels into clusters, has drawn significant attention in practical
applications. Recently, many graph-based clustering methods, which construct an
adjacent graph to model the data relationship, have shown dominant performance.
However, the high dimensionality of HSI data makes it hard to construct the
pairwise adjacent graph. Besides, abundant spatial structures are often
overlooked during the clustering procedure. In order to better handle the high
dimensionality problem and preserve the spatial structures, this paper proposes
a novel unsupervised approach called spatial-spectral clustering with anchor
graph (SSCAG) for HSI data clustering. The SSCAG has the following
contributions: 1) the anchor graph-based strategy is used to construct a
tractable large graph for HSI data, which effectively exploits all data points
and reduces the computational complexity; 2) a new similarity metric is
presented to embed the spatial-spectral information into the combined adjacent
graph, which can mine the intrinsic property structure of HSI data; 3) an
effective neighbors assignment strategy is adopted in the optimization, which
performs the singular value decomposition (SVD) on the adjacent graph to get
solutions efficiently. Extensive experiments on three public HSI datasets show
that the proposed SSCAG is competitive against the state-of-the-art approaches.
Related papers
- A Dual Adaptive Assignment Approach for Robust Graph-Based Clustering [18.614842530666834]
We introduce a new framework called the Dual Adaptive Assignment Approach for Robust Graph-Based Clustering (RDSA)
RDSA consists of three key components: (i) a node embedding module that effectively integrates the graph's topological features and node attributes; (ii) a structure-based soft assignment module that improves graph modularity by utilizing an affinity matrix for node assignments; and (iii) a node-based soft assignment module that identifies community landmarks and refines node assignments to enhance the model's robustness.
Our findings indicate that RDSA provides robust clustering across different graph types, excelling in clustering effectiveness and robustness, including adaptability
arXiv Detail & Related papers (2024-10-29T05:18:34Z) - Multiview Subspace Clustering of Hyperspectral Images based on Graph
Convolutional Networks [12.275530282665578]
This study proposes a multiview subspace clustering of hy-perspectral images (HSI) based on graph convolutional networks.
The model was evaluated on three popular HSI datasets, including Indian Pines, Pavia University, and Houston.
It achieved overall accuracies of 92.38%, 93.43%, and 83.82%, respectively, and significantly outperformed the state-of-the-art clustering methods.
arXiv Detail & Related papers (2024-03-03T10:19:18Z) - Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering.
Models estimate an initial graph beforehand to apply GCN.
Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
arXiv Detail & Related papers (2024-02-25T07:03:37Z) - Contrastive Multi-view Subspace Clustering of Hyperspectral Images based
on Graph Convolutional Networks [14.978666092012856]
Subspace clustering is an effective approach for clustering hyperspectral images.
In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks.
The proposed model effectively improves the clustering accuracy of HSI.
arXiv Detail & Related papers (2023-12-11T02:22:10Z) - Geometry Contrastive Learning on Heterogeneous Graphs [50.58523799455101]
This paper proposes a novel self-supervised learning method, termed as Geometry Contrastive Learning (GCL)
GCL views a heterogeneous graph from Euclidean and hyperbolic perspective simultaneously, aiming to make a strong merger of the ability of modeling rich semantics and complex structures.
Extensive experiments on four benchmarks data sets show that the proposed approach outperforms the strong baselines.
arXiv Detail & Related papers (2022-06-25T03:54:53Z) - Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
We propose an effective and efficient graph learning model for multi-view clustering.
Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm.
Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
arXiv Detail & Related papers (2021-08-15T13:14:28Z) - Spatial-spectral Hyperspectral Image Classification via Multiple Random
Anchor Graphs Ensemble Learning [88.60285937702304]
This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE)
Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region.
Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
arXiv Detail & Related papers (2021-03-25T09:31:41Z) - Multi-Level Graph Convolutional Network with Automatic Graph Learning
for Hyperspectral Image Classification [63.56018768401328]
We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification.
By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions.
Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
arXiv Detail & Related papers (2020-09-19T09:26:20Z) - Graph Convolutional Subspace Clustering: A Robust Subspace Clustering
Framework for Hyperspectral Image [6.332208511335129]
We present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering.
Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain.
We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data.
arXiv Detail & Related papers (2020-04-22T10:09:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.