IMPROVING ACTIVE LEARNING METHODS USING SPATIAL INFORMATION

被引：9

作者：

Pasolli, Edoardo ^{[1
]}

Melgani, Farid ^{[1
]}

Tuia, Devis ^{[2
]}

Pacifici, Fabio ^{[3
]}

Emery, William J. ^{[4
]}

机构：

[1] Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy

[2] Univ Valencia, Image Proc Lab, Valencia, Spain

[3] DigitalGlobe Inc, Colorado Springs, CO USA

[4] Univ Colorado, Dept Aerosp Engn, Colorado Springs, CO USA

来源：

2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年

关键词：

Active learning; spatial information; support vector machines (SVMs); very-high-resolution (VHR) images; CLASSIFICATION;

D O I：

10.1109/IGARSS.2011.6050089

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning.

引用

页码：3923 / 3926

页数：4

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