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In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral...
In this paper, we propose an object-based fusion approach for the joint use of polarimetric synthetic aperture radar (PolSAR) and hyperspectral data. The proposed approach extracts information from both datasets based on an object-level, which is used here for land use classification. The achieved classification result infers that the proposed methodology improves the classification performance of...
In this paper, a novel approach is proposed for the spectral-spatial classification of hyperspectral images. The proposed classification approach is based on a novel filtering technique, here entitled as extended extinction profile (EEP). The proposed classification approach is applied on two well-known data sets: Pavia University and Indian Pines; and the obtained results have been compared with...
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