Remote sensing image scene classification based on multiscale features fusion

Jingyuan He; Bailong Yang; Ruhan A; Luogeng Tian; Yang Su

doi:10.1117/12.2639484

27 June 2022 Remote sensing image scene classification based on multiscale features fusion

Jingyuan He, Bailong Yang, Ruhan A, Luogeng Tian, Yang Su

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 1225314 (2022) https://doi.org/10.1117/12.2639484
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

In order to solve the difficulties of multi-scale feature extraction and weak representation in remote sensing image scene classification, a classification method based on multi-scale feature fusion (MFF) was proposed. The convolutional representation and fully connected features generated by the feature fusion of the MFF are used as high-level features to generate discriminative scene representations, which are then input into the softmax classifier to obtain the semantic labels of scenes. The existing convolutional neural network-based methods and MFF methods are tested on three widelyused datasets. The results show that the MFF method has higher overall accuracy than the existing convolutional neural network-based methods and can better meet the current demand for remote sensing image scene classification.

Citation Download Citation

Jingyuan He, Bailong Yang, Ruhan A, Luogeng Tian, and Yang Su "Remote sensing image scene classification based on multiscale features fusion", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 1225314 (27 June 2022); https://doi.org/10.1117/12.2639484

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KEYWORDS

Scene classification

Image fusion

Remote sensing

Image classification

Classification systems

Feature extraction

Convolution

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