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Learning scene-vectors for remote sensing image scene classification.
- Source :
-
Neurocomputing . Jun2024, Vol. 587, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Representing the scenes by learning the subtle variations in the spatial content of different classes is crucial for scene classification in remote sensing images. In this paper, we propose a scene attribute modeling to obtain a discriminative and compact representation for scene classification. First, we construct a scene attribute model (SAM) by training a Gaussian mixture model (GMM) using convolutional features to capture the scene attributes implicitly. Then, we perform a maximum a posteriori (MAP) adaptation to enhance the contribution of significant attributes in each scene resulting in a high-dimensional feature vector which contains redundant attributes from all the scenes. Hence, we use factor analysis to obtain a compact representation of the high-dimensional feature vector termed scene-vector , which retains only the significant attributes specific to a scene. The proposed approach is demonstrated on three benchmark datasets, namely, UC Merced Land Use, AID, and NWPU-RESISC45 datasets. We further show that, being a compact representation, our scene-vector outperforms state-of-the-art methods for scene classification in remote sensing images. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DISTANCE education
*GAUSSIAN mixture models
*FACTOR analysis
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 587
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 176864511
- Full Text :
- https://doi.org/10.1016/j.neucom.2024.127679