Edge-Inferring Graph Neural Network with Dynamic Task-Guided Self-Diagnosis for Fewshot Hyperspectral Image Classification

Thumbnail Image

Files

Edge-Inferring_Graph_Neural_Network_With_Dynamic_Task-Guided_Self-Diagnosis_for_Few-Shot_Hyperspectral_Image_Classification.pdf (3.67 MB)

Links to Files

https://ieeexplore.ieee.org/abstract/document/9849702

Permanent Link

https://doi.org/10.1109/TGRS.2022.3196311
 http://hdl.handle.net/11603/25547

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-5450-4891

Date

2022-08-04

Type of Work

journal articles
postprints
Text

Department

Program

Citation of Original Publication

C. Yu, J. Huang, M. Song, Y. Wang and C. -I. Chang, "Edge-Inferring Graph Neural Network with Dynamic Task-Guided Self-Diagnosis for Few-shot Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, 2022, doi: 10.1109/TGRS.2022.3196311.

Rights

© 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

Abstract

The current hyperspectral image classification (HSIC) model based on the convolutional neural network for feature extraction and softmax classifier has been prone to the barrier of label prediction with limited samples. Substituting for the enormously complicated work of terrain labeling, few-shot learning provides a popular option for HSIC with very few annotated samples. In this article, we proposed a novel edge-inferring framework with the metalearning paradigm for hyperspectral few-shot classification (HSFSC), in which a graph neural network for similarity measurement is first presented to iteratively infer edge labels with the exploitation of instance-level similarity and the distribution-level similarity. Besides, in the metatraining stage, the pixel prediction model and the patch prediction model based on edge-inferring architecture are concretized jointly to improve the classification accuracy of the test samples. Expressly, at the metatesting phase, the dynamic task-guided self-diagnosis strategy is developed for the first time to diagnose the samples separability of the current classification task, which is responsible for dynamically assigning the most reliable results based on the generated reliability grade of the sample. The extensive experimental results and analysis of three hyperspectral image datasets demonstrate the superiority of the proposed HSFSC architecture compared with other advanced methods.