NDLI: Spectral–Spatial Hyperspectral Image Classification Based on KNN

Content Provider	Springer Nature Link
Author	Huang, Kunshan Li, Shutao Kang, Xudong Fang, Leyuan
Copyright Year	2015
Abstract	Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral–spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods.
Starting Page	1
Ending Page	13
Page Count	13
File Format	PDF
ISSN	15572064
Journal	Sensing and Imaging: An International Journal
Volume Number	17
Issue Number	1
e-ISSN	15572072
Language	English
Publisher	Springer US
Publisher Date	2015-12-12
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Spectral–spatial hyperspectral image classification K nearest neighbor Optimization Support vector machines Electrical Engineering Microwaves, RF and Optical Engineering Imaging Radiology
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Kernel-Based KNN and Gaussian Classifiers for Hyperspectral Image Classification

Spectral–Spatial HyperspectralImage Classification With K-Nearest Neighbor and Guided Filter

Extended Random Walker-Based Classification of Hyperspectral Images

Spectral–Spatial Pixel Characterization Using Gabor Filters for Hyperspectral Image Classification

Hyperspectral Imagery Classification Based on Rotation-Invariant Spectral–Spatial Feature

A Flexible Metric Nearest-Neighbor Classification based on the Decision Boundaries of SVM for Hyperspectral Image

Hyperspectral Image Classification Using Weighted Joint Collaborative Representation

Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine

The effect of training data on hyperspectral classification algorithms

Spectral–Spatial Hyperspectral Image Classification Based on KNN

Similar Documents

Kernel-Based KNN and Gaussian Classifiers for Hyperspectral Image Classification

Spectral–Spatial HyperspectralImage Classification With K-Nearest Neighbor and Guided Filter

Extended Random Walker-Based Classification of Hyperspectral Images

Spectral–Spatial Pixel Characterization Using Gabor Filters for Hyperspectral Image Classification

Hyperspectral Imagery Classification Based on Rotation-Invariant Spectral–Spatial Feature

A Flexible Metric Nearest-Neighbor Classification based on the Decision Boundaries of SVM for Hyperspectral Image

Hyperspectral Image Classification Using Weighted Joint Collaborative Representation

Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine

The effect of training data on hyperspectral classification algorithms

Spectral–Spatial Hyperspectral Image Classification Based on KNN