One-Class Risk Estimation for One-Class Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2210.15457v2
• Date: Fri, 25 Aug 2023 11:45:02 GMT
• Title: One-Class Risk Estimation for One-Class Hyperspectral Image Classification
• Authors: Hengwei Zhao, Yanfei Zhong, Xinyu Wang, Hong Shu
• Abstract summary: Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI. Deep learning-based methods are currently the mainstream to overcome distribution overlap in HSI multiclassification. In this article, a weakly supervised deep HSI one-class classification, HOneCls, is proposed.
• Score: 8.206701378422968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation. However, when one-class classification meets HSI, it is difficult for classifiers to find a balance between the overfitting and underfitting of positive data due to the problems of distribution overlap and distribution imbalance. Although deep learning-based methods are currently the mainstream to overcome distribution overlap in HSI multiclassification, few studies focus on deep learning-based HSI one-class classification. In this article, a weakly supervised deep HSI one-class classifier, namely, HOneCls, is proposed, where a risk estimator,the one-class risk estimator, is particularly introduced to make the fully convolutional neural network (FCN) with the ability of one class classification in the case of distribution imbalance. Extensive experiments (20 tasks in total) were conducted to demonstrate the superiority of the proposed classifier.

Related papers

• Selective Classification Under Distribution Shifts [2.6541808384534478]
  In selective classification, a classifier abstains from making predictions that are likely to be wrong to avoid excessive errors. We propose an SC framework that takes into account distribution shifts. We show that our proposed score functions are more effective and reliable than the existing ones for generalized SC.
  arXiv  Detail & Related papers  (2024-05-08T15:52:50Z)
• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• A Universal Unbiased Method for Classification from Aggregate Observations [115.20235020903992]
  This paper presents a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary losses. Our proposed method not only guarantees the risk consistency due to the unbiased risk estimator but also can be compatible with arbitrary losses.
  arXiv  Detail & Related papers  (2023-06-20T07:22:01Z)
• Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification [18.786429304405097]
  Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. In this study, we investigate recently proposed subspace learning methods for one-class classification (OCC) In this way, there is no separate dimensionality reduction or feature selection procedure needed in the proposed classification framework. Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
  arXiv  Detail & Related papers  (2023-04-19T15:17:05Z)
• Anomaly Detection using Ensemble Classification and Evidence Theory [62.997667081978825]
  We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We use uncertainty for the anomaly detection approach.
  arXiv  Detail & Related papers  (2022-12-23T00:50:41Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning [49.04790688256481]
  The goal of generalized zero-shot learning (GZSL) is to recognise both seen and unseen classes. Most GZSL methods typically learn to synthesise visual representations from semantic information on the unseen classes. We propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features.
  arXiv  Detail & Related papers  (2021-01-09T05:21:27Z)
• Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification [22.806324361016863]
  We propose a novel approach for training deep robust multiclass classifiers that provides adversarial robustness. We show that the regularization of the latent space based on our approach yields excellent classification accuracy.
  arXiv  Detail & Related papers  (2020-10-29T11:15:17Z)
• Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem [3.411873646414169]
  Population Structure-learned classifier (PSC) is proposed. PSC can obtain better generalization performance on IHDLSS. PSC is superior to the state-of-art methods in IHDLSS.
  arXiv  Detail & Related papers  (2020-09-10T08:33:39Z)
• Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification [0.456877715768796]
  This work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving method. Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression.
  arXiv  Detail & Related papers  (2020-05-28T18:58:11Z)

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.