NDLI: Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring

Content Provider	Springer Nature Link
Author	Chen, Zhao Wang, Bin
Copyright Year	2016
Abstract	Recently, a general framework for spectral-spatial classification has caught the attention of the hyperspectral imagery (HSI) society. It consists of three parts: classification, segmentation and combination of the former results to make a refined labeled map. Seeing the potentials of the last part, we derive a novel combination rule based on affinity scoring (CRAS). The core of the system is affinity score (AS), which is derived from fuzzy logic. Every AS measures the degree, i.e., the affinity, by which a pixel belongs to a class. The score is essentially decided by three factors: local spatial consistency, spectral similarity, and prior knowledge. The method is compatible with basic classification and segmentation tools, thus saving the trouble of designing complex techniques for the other parts in the framework. Experimental results show that CRAS excels several basic techniques as well as various state-of-the-art methods in the area of spectral-spatial classification.近年来, 空谱结合的高光谱图像分类方法受到重视。在该领域内, 存在一种分类-分割-合并的框架。为了提升该框架中合并环节的作用, 本文提出一种新型的基于隶属度评分的新的合并方法, 用于空谱结合的分类。该算法的核心是隶属度评分, 其概念由模糊数学中的 “隶属度” 发展而来。一个隶属度分数实际上衡量的是一个像素属于某一个类别的程度。隶属度评分主要受到三个因素的影响: 局部空间一致性, 光谱相似性以及先验知识。提出的合并算法可与基本的经典的分类和分割算法结合使用。实验证明提出的合并算法在分类精度上优于多种不同的空谱结合的经典算法及以当前先进的空谱结合的算法。
Starting Page	1
Ending Page	13
Page Count	13
File Format	PDF
ISSN	1674733X
Journal	Science in China Series : Information Sciences
Volume Number	59
Issue Number	10
e-ISSN	18691919
Language	Chinese
Publisher	Science China Press
Publisher Date	2016-08-26
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	hyperspectral imagery spectral-spatial classification affinity score local spatial consistency fuzzy superpixel Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

Semisupervised Spectral–Spatial Classification of Hyperspectral Imagery With Affinity Scoring

A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

An improved spectral-spatial classification framework for hyperspectral remote sensing images

Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery

Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels

Unsupervised fuzzy classification of multispectral imagery using spatial-spectral features (1997).

Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery

Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring

Similar Documents

Semisupervised Spectral–Spatial Classification of Hyperspectral Imagery With Affinity Scoring

A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

An improved spectral-spatial classification framework for hyperspectral remote sensing images

Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery

Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels

Unsupervised fuzzy classification of multispectral imagery using spatial-spectral features (1997).

Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery

Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring