2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) 2020
DOI: 10.1109/icaica50127.2020.9182577
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Cited by 4 publications
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“…Extracting spatial features in tandem with spectral features has been shown to significantly improve model performance. The use of spatial–spectral features can be achieved through two approaches: (i) by extracting spatial features separately, for example by using a 1D-CNN or 2D-CNN [ 41 , 42 ] and combining the data from the spectral extractor, for example using a recurrent neural network (RNN) or long short-term memory (LSTM) [ 42 , 43 ]; and (ii) by leveraging three-dimensional patches ( p × p × b ) associated with p × p spatial neighborhood pixels and b spectral bands to fully exploit important discriminative patterns in the hyperspectral data cubes. Despite the advances in 3D-CNN architecture, very few studies have utilized this approach for hyperspectral sensing in plant disease scouting [ 44 ].…”
Section: Introductionmentioning
confidence: 99%
“…Extracting spatial features in tandem with spectral features has been shown to significantly improve model performance. The use of spatial–spectral features can be achieved through two approaches: (i) by extracting spatial features separately, for example by using a 1D-CNN or 2D-CNN [ 41 , 42 ] and combining the data from the spectral extractor, for example using a recurrent neural network (RNN) or long short-term memory (LSTM) [ 42 , 43 ]; and (ii) by leveraging three-dimensional patches ( p × p × b ) associated with p × p spatial neighborhood pixels and b spectral bands to fully exploit important discriminative patterns in the hyperspectral data cubes. Despite the advances in 3D-CNN architecture, very few studies have utilized this approach for hyperspectral sensing in plant disease scouting [ 44 ].…”
Section: Introductionmentioning
confidence: 99%
“…[25]. The use of spatial spectral characteristics can be achieved using two approaches: (i) by separately extracting spatial characteristics using CNN [26,27] and combining data from a spectral extractor using RNN, or LSTM [27,28]; and (ii) by using three-dimensional patterns in hyperspectral data cubes (p × p × b) associated with p × p spatially adjacent pixels and b spectral bands to take full advantage of important distinctive patterns.…”
Section: Introductionmentioning
confidence: 99%