دورية أكاديمية
Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data
| العنوان: | Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data |
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| المؤلفون: | Subir Paul, Vinayaraj Poliyapram, Nevrez Imamoglu, Kuniaki Uto, Ryosuke Nakamura, D. Nagesh Kumar |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 1426-1437 (2020) |
| بيانات النشر: | IEEE, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Canopy averaged chlorophyll content (CACC), convolutional autoencoder (CAE), deep learning, Gaussian process regression (GPR), hyperspectral (HS) data, pear orchard, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Chlorophyll content is one of the essential parameters to assess the growth process of the fruit trees. This present study developed a model for estimation of canopy averaged chlorophyll content (CACC) of pear trees using the convolutional autoencoder (CAE) features of hyperspectral (HS) data. This study also demonstrated the inspection of anomaly among the trees by employing multidimensional scaling on the CAE features and detected outlier trees prior to fit nonlinear regression models. These outlier trees were excluded from the further experiments that helped in improving the prediction performance of CACC. Gaussian process regression (GPR) and support vector regression (SVR) techniques were investigated as nonlinear regression models and used for prediction of CACC. The CAE features were proven to be providing better prediction of CACC when compared with the direct use of HS bands or vegetation indices as predictors. The CACC prediction performance was improved with the exclusion of the outlier trees during training of the regression models. It was evident from the experiments that GPR could predict the CACC with better accuracy compared to SVR. In addition, the reliability of the tree canopy masks, which were utilized for averaging the features' values for a particular tree, was also evaluated. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/9050560/; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2020.2983000 |
| URL الوصول: | https://doaj.org/article/021ee32b52ce425c91b578ef0d36ccb1 |
| رقم الأكسشن: | edsdoj.021ee32b52ce425c91b578ef0d36ccb1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21511535 |
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| DOI: | 10.1109/JSTARS.2020.2983000 |