Probabilistic Deep Metric Learning for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2211.08349v1
• Date: Tue, 15 Nov 2022 17:57:12 GMT
• Title: Probabilistic Deep Metric Learning for Hyperspectral Image Classification
• Authors: Chengkun Wang, Wenzhao Zheng, Xian Sun, Jiwen Lu, Jie Zhou
• Abstract summary: This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
• Score: 91.5747859691553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a probabilistic deep metric learning (PDML) framework for hyperspectral image classification, which aims to predict the category of each pixel for an image captured by hyperspectral sensors. The core problem for hyperspectral image classification is the spectral variability between intraclass materials and the spectral similarity between interclass materials, motivating the further incorporation of spatial information to differentiate a pixel based on its surrounding patch. However, different pixels and even the same pixel in one patch might not encode the same material due to the low spatial resolution of most hyperspectral sensors, leading to an inconsistent judgment of a specific pixel. To address this issue, we propose a probabilistic deep metric learning framework to model the categorical uncertainty of the spectral distribution of an observed pixel. We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions. We treat each pixel in a patch as a training sample, enabling us to exploit more information from the patch compared with conventional methods. Our framework can be readily applied to existing hyperspectral image classification methods with various network architectures and loss functions. Extensive experiments on four widely used datasets including IN, UP, KSC, and Houston 2013 datasets demonstrate that our framework improves the performance of existing methods and further achieves the state of the art. Code is available at: https://github.com/wzzheng/PDML.

Related papers

• Dual-stage Hyperspectral Image Classification Model with Spectral Supertoken [15.426635239291729]
  We introduce the Dual-stage Spectral Supertoken (DSTC), inspired by superpixel concepts. DSTC employs spectrum-derivative-based pixel clustering to group pixels with similar spectral characteristics into spectral supertokens. We also propose a class-proportion-based soft label, which adaptively assigns weights to different categories based on their prevalence.
  arXiv  Detail & Related papers  (2024-07-10T01:58:30Z)
• A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images [0.0]
  Food engineers use hyperspectral images to classify the type and quality of a food sample. In order to train these methods, every pixel in each training image needs to be labelled. A consensus-constrained parsimonious Gaussian mixture model (ccPGMM) is proposed to label pixels in hyperspectral images.
  arXiv  Detail & Related papers  (2024-03-05T22:23:43Z)
• Learning Invariant Inter-pixel Correlations for Superpixel Generation [12.605604620139497]
  Learnable features exhibit constrained discriminative capability, resulting in unsatisfactory pixel grouping performance. We propose the Content Disentangle Superpixel algorithm to selectively separate the invariant inter-pixel correlations and statistical properties. The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-28T09:46:56Z)
• Semi-supervised segmentation of land cover images using nonlinear canonical correlation analysis with multiple features and t-SNE [1.7000283696243563]
  Image segmentation is a clustering task whereby each pixel is assigned a cluster label. In this work, by resorting to label only a small quantity of pixels, a new semi-supervised segmentation approach is proposed. The proposed semi-supervised RBF-CCA algorithm has been implemented on several remotely sensed multispectral images.
  arXiv  Detail & Related papers  (2024-01-22T17:56:07Z)
• Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification [9.479240476603353]
  We propose to augment the prototype network with TransMix for few-shot hyperspectral image classification(APNT) While taking the prototype network as the backbone, it adopts the transformer as feature extractor to learn the pixel-to-pixel relation. The proposed method has demonstrated sate of the art performance and better robustness for few-shot hyperspectral image classification.
  arXiv  Detail & Related papers  (2024-01-22T06:56:52Z)
• OsmLocator: locating overlapping scatter marks with a non-training generative perspective [48.50108853199417]
  Locating overlapping marks faces many difficulties such as no texture, less contextual information, hallow shape and tiny size. Here, we formulate it as a optimization problem on clustering-based re-visualization from a non-training generative perspective. We especially built a dataset named 2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods.
  arXiv  Detail & Related papers  (2023-12-18T12:39:48Z)
• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• 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)
• ITSELF: Iterative Saliency Estimation fLexible Framework [68.8204255655161]
  Saliency object detection estimates the objects that most stand out in an image. We propose a superpixel-based ITerative Saliency Estimation fLexible Framework (ITSELF) that allows any user-defined assumptions to be added to the model. We compare ITSELF to two state-of-the-art saliency estimators on five metrics and six datasets.
  arXiv  Detail & Related papers  (2020-06-30T16:51:31Z)

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.