A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data-Reference-Cited by-同舟云学术

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A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data

Published:2017-03 Issue: Volume:63 Page:229-245
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Appice Annalisa,Guccione Pietro,Malerba Donato

Funder

European Commission through the project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference61 articles.

1. Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system;Appice;Mach. Learn. J.,2016

2. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations;Benediktsson;IEEE Trans. Geosci. Remote Sens.,2003

3. Pixel, object and hybrid classification comparisons;Bernardini;J. Spat. Sci.,2010

4. A. Blum, T. in: Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the 11th Workshop on Computational Learning Theory, COLT 1998, 1998.

5. F. Bovolo, L. Bruzzone, M. Marconcini, A novel context-sensitive SVM for classification of remote sensing images, in: Proceedings of the 2006 IEEE International Conference on Geoscience and Remote Sensing Symposium, IGARSS 2006, 2006, pp. 2498–2501.

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