Learning scene-vectors for remote sensing image scene classification

• Published in: Neurocomputing (Amsterdam) Vol. 587; p. 127679
• Main Authors: Datla, Rajeshreddy, Perveen, Nazil, C., Krishna Mohan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.06.2024
• Subjects: Remote sensing images; Scene attribute model; Scene classification; Scene-vector; Scene-vector; Scene attribute model; Scene classification; Remote sensing images
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2024.127679

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.