Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering

• URL: http://arxiv.org/abs/2312.15447v1
• Date: Sun, 24 Dec 2023 09:54:40 GMT
• Title: Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering
• Authors: Kangning Cui, Ruoning Li, Sam L. Polk, Yinyi Lin, Hongsheng Zhang, James M. Murphy, Robert J. Plemmons, Raymond H. Chan
• Abstract summary: This paper introduces a novel unsupervised HSI clustering algorithm, Superpixel-based and Spatially-regularized Diffusion Learning (S2DL) S2DL incorporates rich spatial information encoded in HSIs into diffusion geometry-based clustering. S2DL's performance is illustrated with extensive experiments on three publicly available, real-world HSIs.
• Score: 4.643572021927615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral images (HSIs) provide exceptional spatial and spectral resolution of a scene, crucial for various remote sensing applications. However, the high dimensionality, presence of noise and outliers, and the need for precise labels of HSIs present significant challenges to HSIs analysis, motivating the development of performant HSI clustering algorithms. This paper introduces a novel unsupervised HSI clustering algorithm, Superpixel-based and Spatially-regularized Diffusion Learning (S2DL), which addresses these challenges by incorporating rich spatial information encoded in HSIs into diffusion geometry-based clustering. S2DL employs the Entropy Rate Superpixel (ERS) segmentation technique to partition an image into superpixels, then constructs a spatially-regularized diffusion graph using the most representative high-density pixels. This approach reduces computational burden while preserving accuracy. Cluster modes, serving as exemplars for underlying cluster structure, are identified as the highest-density pixels farthest in diffusion distance from other highest-density pixels. These modes guide the labeling of the remaining representative pixels from ERS superpixels. Finally, majority voting is applied to the labels assigned within each superpixel to propagate labels to the rest of the image. This spatial-spectral approach simultaneously simplifies graph construction, reduces computational cost, and improves clustering performance. S2DL's performance is illustrated with extensive experiments on three publicly available, real-world HSIs: Indian Pines, Salinas, and Salinas A. Additionally, we apply S2DL to landscape-scale, unsupervised mangrove species mapping in the Mai Po Nature Reserve, Hong Kong, using a Gaofen-5 HSI. The success of S2DL in these diverse numerical experiments indicates its efficacy on a wide range of important unsupervised remote sensing analysis tasks.

Related papers

• Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images [64.72242126879503]
  Hyperspectral images (HSI) clustering is an important but challenging task. We first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI. We then design a superpixel graph contrastive clustering model to learn discriminative superpixel representations.
  arXiv  Detail & Related papers  (2024-03-04T07:40:55Z)
• Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion [67.35540259040806]
  We propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DCNet. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components. We append a self-supervised learning module behind the CSU net by guaranteeing the material consistency to enhance the detailed appearances of the restored HS product.
  arXiv  Detail & Related papers  (2022-05-07T23:40:36Z)
• Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry [6.279792995020646]
  This work introduces the Spatial-Spectral Image Reconstruction and Clustering with Diffusion Geometry (DSIRC) algorithm for partitioning highly mixed hyperspectral images. DSIRC locates spectrally correlated pixels within a data-adaptive spatial neighborhood and reconstructs that pixel's spectral signature using those of its neighbors. Results indicate that incorporating spatial information through image reconstruction substantially improves the performance of pixel-wise clustering.
  arXiv  Detail & Related papers  (2022-04-28T13:42:12Z)
• Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification [49.32130776974202]
  SP-DLRR is composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-25T10:47:26Z)
• Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction [64.10636296274168]
  Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic image (PAN) to generate an enhanced HSI with high spectral and spatial resolution. Recently proposed HS pansharpening methods have obtained remarkable results using deep convolutional networks (ConvNets) We propose a novel over-complete network, called HyperKite, which focuses on learning high-level features by constraining the receptive from increasing in the deep layers.
  arXiv  Detail & Related papers  (2021-07-06T14:11:03Z)
• Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks [41.78245271989529]
  We propose a graph convolution network (GCN) based framework for HSI classification. In particular, we first cluster the pixels with similar spectral features into a superpixel and build the graph based on the superpixels of the input HSI. We then partition it into several sub-graphs by pruning the edges with weak weights, so as to strengthen the correlations of nodes with high similarity.
  arXiv  Detail & Related papers  (2020-12-20T14:16:59Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)

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.