Hyperspectral Image Classification Using Fuzzy C-Means Based Composite Kernel Approach

被引：0

作者：

Sigirci, Ibrahim Onur ^{[1
]}

Bilgin, Gokhan ^{[1
]}

机构：

[1] Yildiz Tekn Univ, Bilgisayar Muhendisligi Bolumu, TR-34220 Istanbul, Turkey

来源：

2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2017年

关键词：

Fuzzy c-means; extreme learning machine; composite kernels; support vector machines; spectral and spatial information; EXTREME LEARNING-MACHINE;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In the classification of high-dimensional hyperspectral images, only spectral information is not sufficient to obtain successful results when the number of training data is small. In this case, spatial information can be exploited as well as spectral information. For this purpose, we aimed to use spatial information obtained from the fuzzy C-means (FCM) algorithm and spectral information together with the help of composite kernels to classify hyperspectral images. The composite kernels obtained in experimental studies are used for classification purposes by using extreme learning machines (ELM) and support vector machines (SVM); in addition to that, the results were presented comparatively in the tables.

引用

页数：4

共 20 条

[1]

[Anonymous], 2002, Matrices: Theory and Applications

[2]

[Anonymous], 2007, THESIS I NATL POLYT

[3] Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis [J].

Arzuaga-Cruz, E ;

Jimenez-Rodriguez, LO ;

Vélez-Reyes, M .

ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL AND ULTRASPECTRAL IMAGERY IX, 2003, 5093 :462-473

[4] Kernel-based methods for hyperspectral image classification [J].

Camps-Valls, G ;

Bruzzone, L .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (06) :1351-1362

[5] Hyperspectral Image Classification via Kernel Sparse Representation [J].

Chen, Yi ;

Nasrabadi, Nasser M. ;

Tran, Trac D. .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (01) :217-231

[6] Tree Species Classification in Boreal Forests With Hyperspectral Data [J].

Dalponte, Michele ;

Orka, Hans Ole ;

Gobakken, Terje ;

Gianelle, Damiano ;

Naesset, Erik .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (05) :2632-2645

[7] View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification [J].

Di, Wei ;

Crawford, Melba M. .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2012, 50 (05) :1942-1954

[8]

Fauvel M, 2006, 2006 IEEE INT C AC S, V2

[9] Investigation of the random forest framework for classification of hyperspectral data [J].

Ham, J ;

Chen, YC ;

Crawford, MM ;

Ghosh, J .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (03) :492-501

[10] Extreme learning machine: Theory and applications [J].

Huang, Guang-Bin ;

Zhu, Qin-Yu ;

Siew, Chee-Kheong .

NEUROCOMPUTING, 2006, 70 (1-3) :489-501

← 1 2 →