Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing
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Otros documentos de la autoría: Fernandez-Beltran, Ruben; Demir, Begüm; Pla, Filiberto; Plaza, Antonio
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Título
Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic HashingFecha de publicación
2020-02-06Editor
Institute of Electrical and Electronics EngineersISSN
1545-598XCita bibliográfica
FERNANDEZ-BELTRAN, Ruben, et al. Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing. IEEE Geoscience and Remote Sensing Letters, 2020.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Unsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and compu ... [+]
Unsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and computation. Although existing unsupervised hashing methods are suitable for operational applications, they exhibit limitations when accurately modeling the complex semantic content present in RS images using binary codes (in an unsupervised manner). To address this problem, in this letter, we introduce a novel unsupervised hashing method that takes advantage of the generative nature of probabilistic topic models to encapsulate the hidden semantic patterns of the data into the final binary representation. Specifically, we introduce a new probabilistic latent semantic hashing (pLSH) model to effectively learn the hash codes using three main steps: 1) data grouping, where the input RS archive is clustered into several groups; 2) topic computation, where the pLSH model is used to uncover highly descriptive hidden patterns from each group; and 3) hash code generation, where the data probability distributions are thresholded to generate the final binary codes. Our experimental results, obtained on two benchmark archives, reveal that the proposed method significantly outperforms state-of-the-art unsupervised hashing methods. [-]
Proyecto de investigación
APOSTD/2017/007, t ESP2016-79503-C2-2-P, RTI2018-098651-B-C54, TIN2015-63646-C5-5-R, GR18060, H2020 EOXPOSURE Project/734541, European Research Council through the ERC Starting Grant BigEarth under Grant 759764Derechos de acceso
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