SOIL TEXTURE CLASSIFICATION WITH 1D CONVOLUTIONAL NEURAL NETWORKS BASED ON HYPERSPECTRAL DATA

20 citations as recorded by crossref.

1. Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy Y. Wang et al. 10.3390/s20247078
2. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies P. Maier et al. 10.3390/rs13040718
3. A deep residual convolutional neural network for mineral classification N. Agrawal & H. Govil 10.1016/j.asr.2022.12.028
4. Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks A. Santos et al. 10.3390/s20205762
5. Deep Learning for Chemometric Analysis of Plastic Spectral Data from Infrared and Raman Databases E. Neo et al. 10.2139/ssrn.4134033
6. Mark-Spectra: A convolutional neural network for quantitative spectral analysis overcoming spatial relationships Y. Wang et al. 10.1016/j.compag.2021.106624
7. Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones A. Tegegne & G. Angiulli 10.1155/2022/6372089
8. Impact of Exposure and Illumination on Texture Classification Based on Raw Spectral Filter Array Images O. Elezabi et al. 10.3390/s23125443
9. Deep Learning for Land Cover Change Detection O. Sefrin et al. 10.3390/rs13010078
10. Food and agro-product quality evaluation based on spectroscopy and deep learning: A review X. Zhang et al. 10.1016/j.tifs.2021.04.008
11. Construction of complex features for predicting soil total nitrogen content based on convolution operations Y. Wang et al. 10.1016/j.still.2021.105109
12. Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network N. Mohd Yusof et al. 10.1007/s11030-021-10289-1
13. Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model A. Soni et al. 10.1016/j.foodres.2021.110577
14. A deep learning-based method for screening soil total nitrogen characteristic wavelengths Y. Wang et al. 10.1016/j.compag.2021.106228
15. Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks L. Zhong et al. 10.1016/j.geoderma.2021.115366
16. Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area J. Florath & S. Keller 10.3390/rs14030657
17. Physics-guided neural network for predicting chemical signatures C. Murphy & J. Kerekes 10.1364/AO.420688
18. Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery S. Hong et al. 10.1080/15481603.2022.2037887
19. Soil-MobiNet: A Convolutional Neural Network Model Base Soil Classification to Determine Soil Morphology and Its Geospatial Location E. Gyasi & S. Purushotham 10.3390/s23156709
20. 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures C. Murphy & J. Kerekes 10.1255/jsi.2021.a2