Το έργο με τίτλο Hyperspectral image classification with tensor-based rank-R learning models από τον/τους δημιουργό/ούς Makantasis Konstantinos, Voulodimos, Athanasios, Doulamis, Anastasios, Doulamis Nikolaos D., Georgoulas Ioannis διατίθεται με την άδεια Creative Commons Αναφορά Δημιουργού 4.0 Διεθνές
Βιβλιογραφική Αναφορά
K. Makantasis, A. Voulodimos, A. Doulamis, N. Doulamis and I. Georgoulas, "Hyperspectral image classification with tensor-based rank-R learning models," in 26th IEEE International Conference on Image Processing, 2019, pp. 3148-3152. doi: 10.1109/ICIP.2019.8803268
https://doi.org/10.1109/ICIP.2019.8803268
In this paper, we present a general tensor-based nonlinear classifier, the Rank-R Feedforward Neural Network (FNN). In the proposed model, which is an extension of the Rank-1 FNN classifier, the network weights are constrained to satisfy a rank-R Canonical Polyadic Decomposition. By allowing a rank-R, instead of a rank-1, Canonical Polyadic Decomposition of the weights, the learning capacity of the model can be increased, which contributes to avoiding underfitting problems. The effectiveness of the proposed model is scrutinized on a hyperspectral image classification experimental setting, since hyperspectral data can naturally be represented as tensor objects. Performance evaluation results indicate that the proposed model outperforms other state-of-the-art models, including deep learning ones, especially in cases where the number of available training samples is small.