Manifold Learning for Hyperspectral Images

• URL: http://arxiv.org/abs/2503.15016v1
• Date: Wed, 19 Mar 2025 09:12:56 GMT
• Title: Manifold Learning for Hyperspectral Images
• Authors: Fethi Harkat, Tiphaine Deuberet, Guillaume Gey, Valérie Perrier, Kévin Polisano,
• Abstract summary: We propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection.<n>This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection. This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms, particularly in processing Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results.

Related papers

• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis [11.612069983959985]
  We propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks.
  arXiv  Detail & Related papers  (2024-07-22T01:20:32Z)
• A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data [5.1901440366375855]
  The use of Deep Learning techniques for classification in Hyperspectral Imaging (HSI) is rapidly growing. Recent methods have also explored the usage of Graph Convolution Networks and their unique ability to use node features in prediction.
  arXiv  Detail & Related papers  (2023-10-16T00:42:25Z)
• Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography [0.0]
  We introduce a novel method that incorporates separate deep learning models at each stage of the tomography pipeline-projection, sinogram, and reconstruction-to address specific artifacts locally in a data-driven way.<n>Our approach includes bypass connections that feed both the outputs from previous stages and raw data to subsequent stages, minimizing the risk of error propagation.
  arXiv  Detail & Related papers  (2023-09-01T14:40:25Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Graph Embedding via High Dimensional Model Representation for Hyperspectral Images [9.228929858529678]
  Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes. Manor learning methods have shown excellent performance to deal with hyperspectral image (HSI) analysis. A common assumption to deal with the problem is that the transformation between the high-dimensional input space and the (typically low) latent space is linear. The proposed method is compared to manifold learning methods along with its linear counterparts and achieves promising performance in terms of classification accuracy of a representative set of hyperspectral images.
  arXiv  Detail & Related papers  (2021-11-29T16:42:15Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution [20.014293172511074]
  This paper proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior. Experiments on two types of datasets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method.
  arXiv  Detail & Related papers  (2020-11-19T07:52:45Z)
• 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)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.