Active learning (AL) is one of the popular approaches that can mitigate some of the drawbacks of supervised classification. Although sparse representation classifier (SRC) has already proven to be a robust classifier and successfully used in many applications, it is seldom used jointly with AL. In this letter, we propose a novel AL technique for SRCs. In the proposed model, the query function is designed by combining uncertainty and diversity criteria, both of which are defined by using the SRC in kernel space. The proposed technique outperforms other state-of-the-art methods in terms of classification performance.

Active Learning for Hyperspectral Image Classification Using Kernel Sparse Representation Classifiers / Bortiew, Amos; Patra, Swarnajyoti; Bruzzone, Lorenzo. - In: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. - ISSN 1545-598X. - 20:(2023), pp. 55035051-55035055. [10.1109/LGRS.2023.3264283]

Active Learning for Hyperspectral Image Classification Using Kernel Sparse Representation Classifiers

Patra, Swarnajyoti;Bruzzone, Lorenzo
2023-01-01

Abstract

Active learning (AL) is one of the popular approaches that can mitigate some of the drawbacks of supervised classification. Although sparse representation classifier (SRC) has already proven to be a robust classifier and successfully used in many applications, it is seldom used jointly with AL. In this letter, we propose a novel AL technique for SRCs. In the proposed model, the query function is designed by combining uncertainty and diversity criteria, both of which are defined by using the SRC in kernel space. The proposed technique outperforms other state-of-the-art methods in terms of classification performance.
2023
Bortiew, Amos; Patra, Swarnajyoti; Bruzzone, Lorenzo
Active Learning for Hyperspectral Image Classification Using Kernel Sparse Representation Classifiers / Bortiew, Amos; Patra, Swarnajyoti; Bruzzone, Lorenzo. - In: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. - ISSN 1545-598X. - 20:(2023), pp. 55035051-55035055. [10.1109/LGRS.2023.3264283]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/400182
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