![]() ![]() Provided correct acknowledgement is given. If you are an author contributing to an RSC publication, you do not need to request permission Please go to the Copyright Clearance Center request page. To request permission to reproduce material from this article in a commercial publication, Provided that the correct acknowledgement is given and it is not used for commercial purposes. This article in other publications, without requesting further permission from the RSC, ![]() Zhu,Ĭreative Commons Attribution-NonCommercial 3.0 Unported Licence. The results confirmed the usefulness of the proposed attention mechanism-based convolutional network for selecting the most effective band combination of hyperspectral images.Įffective band selection of hyperspectral image by an attention mechanism-based convolutional network In addition, different from the traditional methods, the proposed band selection algorithm can effectively select bands while carrying out model training and can simultaneously take advantage of the original spectral–spatial information. Results showed that the effective bands selected by the proposed attention-based model achieved higher regression R 2 values and classification accuracies not only than the full-spectrum data, but also than the comparative band selection methods, including traditional SPA (successive projections algorithm) and GA (genetic algorithm) methods and the latest 2B-CNN algorithm. A number of calibration models, including SVM, 1D-CNN, 2B-CNN (two-branch CNN), 3D ResNet and the developed network were established for performance comparison. The proposed network was evaluated by two datasets, a regression dataset for predicting the relative chlorophyll content (soil and plant analyzer development, SPAD) of basil leaves and a classification dataset for detecting the drought stress of pepper leaves. The learned band attention vector was adopted as the basis of a band importance indicator to select effective bands. In this paper, an end-to-end trainable network framework that combines band selection, feature extraction, and model training was proposed based on a 3D CNN (convolutional neural network, CNN) with the attention mechanism embedded in its first layer. However, current band selection methods act as a separate procedure before model training and are implemented merely based on extracted average spectra without incorporating spatial information. The selection of effective and representative spectral bands is extremely important in eliminating redundant information and reducing the computational burden for the potential real-time applications of hyperspectral imaging. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |