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CC BY V4.0, Open Access
Journal article
MCE-ST: Classifying crop stress using hyperspectral data with a multiscale conformer encoder and spectral-based tokens
International journal of applied earth observation and geoinformation, Vol.118, Art. 103286
2023
Abstract
Recently, transformers have achieved great success in a number of computer vision tasks due to their excellent ability to capture long-range feature dependencies. In contrast, convolutional neural networks (CNNs) are good at extracting local features. Given that the capture of short- and long-range band dependencies are both important for hyperspectral data classification, we propose MCE-ST, a convolution-transformer (conformer) based framework capable of exploiting the complementary strengths of transformers and CNNs. In contrast to the conventional transformer, which uses a linear projection for tokenization, the proposed MCE-ST uses a convolution-based tokenization method to extract local dependency between spectral bands. Moreover, since different hyperspectral samples may have different spans of local relationships, a multiscale conformer encoder (MCE) comprising two separate branches of depth-wise dilated convolution with different kernel sizes is used to extract the different spans of the local interactions between tokens. We conducted experiments on four salt stress datasets and one cassava disease dataset. The results show that the proposed MCE-ST outperforms the state-of-the-art techniques for crop stress classification using hyperspectral data. The code for MCE-ST is publicly available at https://github.com/Weejaa04/MCE-ST-GitHub.
Details
- Title
- MCE-ST: Classifying crop stress using hyperspectral data with a multiscale conformer encoder and spectral-based tokens
- Authors/Creators
- W.N. Khotimah - Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, 6009, WA, AustraliaM. Bennamoun - Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, 6009, WA, AustraliaF. Boussaid - Department of Electrical, Electronic and Computer Engineering, The University of Western, Australia, 35 Stirling Highway, Crawley, Perth, 6009, WA, AustraliaLian Xu - Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, 6009, WA, AustraliaDavid Edwards - School of Biological Sciences and Centre for Applied Bioinformatics, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, 6009, WA, AustraliaFerdous A Sohel
- Publication Details
- International journal of applied earth observation and geoinformation, Vol.118, Art. 103286
- Publisher
- Elsevier B.V
- Identifiers
- 991005567870007891
- Copyright
- © 2023 The Authors.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology
- Language
- English
- Resource Type
- Journal article
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- 4 Electrical Engineering, Electronics & Computer Science
- 4.169 Remote Sensing
- 4.169.91 Vegetation Mapping
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- Geosciences