NDLI: Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs

Content Provider	IET Digital Library
Author	Ge, Haimiao Wang, Liguo Liu, Yanzhong Li, Cheng Chen, Ruixin
Abstract	An improved version of supervised locally linear embedding is proposed. In this algorithm, the weight factors of the supervised method are adaptively achieved. This method can simplify the supervised feature extraction algorithm by reducing parameters. To improve classification accuracy, a clustering-based fuzzy support vector machine (FSVM) is proposed. Different from traditional FSVMs, the proposed method constructs the fuzzy weights by inner-class clusters. In the proposed method, loose density is defined to express the compactness of the inner-class clusters. The proposed algorithm can restrain the noise and outliers by exploiting the method of endowing with smaller weight for big loose density and bigger weight for the small loose density of samples in the clusters. To inspect the performance of the proposed methods, we conduct experiments on two hyper-spectral images. Results show that the two methods are competitive among the competitors.
Starting Page	941
Ending Page	947
Page Count	7
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0987
Journal	IET Image Processing
Publisher Date	2018-01-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive-weighted LLE Clustering-based FSVM Combinatorial Mathematics Computer Vision And Image Processing Technique Feature Extraction Fuzzy Set Theory Hyperspectral Image Hyperspectral Image Classification Image Classification Image Recognition Knowledge Engineering Technique Supervised Feature Extraction Algorithm Supervised Locally Linear Embedding Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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