Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 563661
Title SVM active learning approach for image classification using spatial information
Author(s) Pasolli, Edoardo; Melgani, Farid; Tuia, Devis; Pacifici, Fabio; Emery, William J.
Source IEEE Transactions on Geoscience and Remote Sensing 52 (2014)4. - ISSN 0196-2892 - p. 2217 - 2223.
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) Active learning - Image classification - Spatial information - Support vector machine (SVM) - Very high resolution (VHR)

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem in the spectral domain only. However, remote sensing images are intrinsically defined both in the spectral and spatial domains. In this paper, we explore this fact by proposing a new active learning approach for support vector machine classification. In particular, we suggest combining spectral and spatial information directly in the iterative process of sample selection. For this purpose, three criteria are proposed to favor the selection of samples distant from the samples already composing the current training set. In the first strategy, the Euclidean distances in the spatial domain from the training samples are explicitly computed, whereas the second one is based on the Parzen window method in the spatial domain. Finally, the last criterion involves the concept of spatial entropy. Experiments on two very high resolution images show the effectiveness of regularization in spatial domain for active learning purposes.

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