20.500.12556/DKUM-89923 Background purification framework with extended morphological attribute profile for hyperspectral anomaly detection Hyperspectral anomaly detection has attracted extensive interests for its wide use in military and civilian fields, and three main categories of detection methods have been developed successively over past few decades, including statistical model-based, representation-based, and deep-learning-based methods. Most of these algorithms are essentially trying to construct proper background profiles, which describe the characteristics of background and then identify the pixels that do not conform to the profiles as anomalies. Apparently, the crucial issue is how to build an accurate background profile; however, the background profiles constructed by existing methods are not accurate enough. In this article, a novel and universal background purification framework with extended morphological attribute profiles is proposed. It explores the spatial characteristic of image and removes suspect anomaly pixels from the image to obtain a purified background. Moreover, three detectors with this framework covering different categories are also developed. The experiments implemented on four real hyperspectral images demonstrate that the background purification framework is effective, universal, and suitable. Furthermore, compared with other popular algorithms, the detectors with the framework perform well in terms of accuracy and efficiency. detectors anomaly detection image reconstruction hyperspectral imaging training optics dictionaries background purification extended attribute profile sparse representation stacked autoencoder detektorji zaznavanje anomalij rekonstrukcija slik spektralna analiza trening optika slovarji true false true IEEE Angleški jezik Slovenski jezik Znanstveno delo 2024-08-19 11:50:41 2024-08-19 12:35:50 2024-08-20 03:22:03 0000-00-00 00:00:00 2021 0 0 Str. 8113-8124 Letn. 14 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-05-11 2021-08-01 2021-08-10 53 74469123 10.1109/JSTARS.2021.3103858 1939-1404 Huang-2021-Background_Purification_Framework_W.pdf Huang-2021-Background_Purification_Framework_W.pdf 1 98BB8F7CFB5160FE7EACD0739409E6BA 838d43ea859fd220c788638ae523319420bb7cfe20377daf1c0f67082663165d 93d11bc8-5e15-11ef-9891-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=182626 https://ieeexplore.ieee.org/document/9511250 0 78db765e-5e16-11ef-9891-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=182628 Fakulteta za naravoslovje in matematiko 0 0 0