Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387048790> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W4387048790 endingPage "165" @default.
- W4387048790 startingPage "154" @default.
- W4387048790 abstract "Crop classification from hyperspectral remote sensing images is an effective means to understand the agricultural scenario of the country. Band selection (BS) is a necessary step to reduce the dimensions of the hyperspectral image. We propose a band selection method that takes into account the image quality in terms of a non-reference quality index along with correlation analysis. The optimum bands selected using the proposed method are then fed to the three supervised machine learning classifiers, namely, support vector machine, K-nearest neighbours and random forest. We have also investigated the impact of correlation analysis by showing the comparison of the proposed band selection method with another variant of our method where correlation analysis is not included. The result shows that the crop classification shows better performance in terms of overall accuracy and kappa coefficient when image quality and correlation analysis are both considered while selecting optimum bands. All the experiments have been performed on the three hyperspectral datasets, Indian Pines, Salinas and AVIRIS-NG, which contain major crop classes. The results show that the optimum bands selected using the proposed method provide the highest overall accuracy, equal to 89.63% (Indian Pines), 95.88% (Salinas) and 97.44% (AVIRIS-NG). The overall accuracy shows a rise from +2% to +4% to that of bands without considering correlation analysis. The advantage of this band selection method is that it does not require any prior knowledge about the crop to select the bands." @default.
- W4387048790 created "2023-09-27" @default.
- W4387048790 creator A5006449545 @default.
- W4387048790 creator A5083271137 @default.
- W4387048790 date "2023-01-01" @default.
- W4387048790 modified "2023-09-27" @default.
- W4387048790 title "Classification of Crops Based on Band Quality and Redundancy from the Hyperspectral Image" @default.
- W4387048790 cites W1558806413 @default.
- W4387048790 cites W1971637299 @default.
- W4387048790 cites W1982471090 @default.
- W4387048790 cites W2037798659 @default.
- W4387048790 cites W2043665634 @default.
- W4387048790 cites W2150705511 @default.
- W4387048790 cites W2153747028 @default.
- W4387048790 cites W2171350553 @default.
- W4387048790 cites W2318153520 @default.
- W4387048790 cites W2444185468 @default.
- W4387048790 cites W2935991947 @default.
- W4387048790 cites W2997988721 @default.
- W4387048790 cites W3047317383 @default.
- W4387048790 cites W3090548235 @default.
- W4387048790 cites W3092261919 @default.
- W4387048790 cites W3142104381 @default.
- W4387048790 cites W4213253873 @default.
- W4387048790 cites W4307515611 @default.
- W4387048790 doi "https://doi.org/10.1007/978-3-031-43605-5_12" @default.
- W4387048790 hasPublicationYear "2023" @default.
- W4387048790 type Work @default.
- W4387048790 citedByCount "0" @default.
- W4387048790 crossrefType "book-chapter" @default.
- W4387048790 hasAuthorship W4387048790A5006449545 @default.
- W4387048790 hasAuthorship W4387048790A5083271137 @default.
- W4387048790 hasConcept C111919701 @default.
- W4387048790 hasConcept C114700698 @default.
- W4387048790 hasConcept C115961682 @default.
- W4387048790 hasConcept C117220453 @default.
- W4387048790 hasConcept C119857082 @default.
- W4387048790 hasConcept C12267149 @default.
- W4387048790 hasConcept C152124472 @default.
- W4387048790 hasConcept C153180895 @default.
- W4387048790 hasConcept C154945302 @default.
- W4387048790 hasConcept C159078339 @default.
- W4387048790 hasConcept C163864269 @default.
- W4387048790 hasConcept C169258074 @default.
- W4387048790 hasConcept C205649164 @default.
- W4387048790 hasConcept C2524010 @default.
- W4387048790 hasConcept C2780092901 @default.
- W4387048790 hasConcept C33923547 @default.
- W4387048790 hasConcept C41008148 @default.
- W4387048790 hasConcept C62649853 @default.
- W4387048790 hasConcept C81917197 @default.
- W4387048790 hasConceptScore W4387048790C111919701 @default.
- W4387048790 hasConceptScore W4387048790C114700698 @default.
- W4387048790 hasConceptScore W4387048790C115961682 @default.
- W4387048790 hasConceptScore W4387048790C117220453 @default.
- W4387048790 hasConceptScore W4387048790C119857082 @default.
- W4387048790 hasConceptScore W4387048790C12267149 @default.
- W4387048790 hasConceptScore W4387048790C152124472 @default.
- W4387048790 hasConceptScore W4387048790C153180895 @default.
- W4387048790 hasConceptScore W4387048790C154945302 @default.
- W4387048790 hasConceptScore W4387048790C159078339 @default.
- W4387048790 hasConceptScore W4387048790C163864269 @default.
- W4387048790 hasConceptScore W4387048790C169258074 @default.
- W4387048790 hasConceptScore W4387048790C205649164 @default.
- W4387048790 hasConceptScore W4387048790C2524010 @default.
- W4387048790 hasConceptScore W4387048790C2780092901 @default.
- W4387048790 hasConceptScore W4387048790C33923547 @default.
- W4387048790 hasConceptScore W4387048790C41008148 @default.
- W4387048790 hasConceptScore W4387048790C62649853 @default.
- W4387048790 hasConceptScore W4387048790C81917197 @default.
- W4387048790 hasLocation W43870487901 @default.
- W4387048790 hasOpenAccess W4387048790 @default.
- W4387048790 hasPrimaryLocation W43870487901 @default.
- W4387048790 hasRelatedWork W2028628118 @default.
- W4387048790 hasRelatedWork W2041399278 @default.
- W4387048790 hasRelatedWork W2051197289 @default.
- W4387048790 hasRelatedWork W2136184105 @default.
- W4387048790 hasRelatedWork W2336974148 @default.
- W4387048790 hasRelatedWork W2897187994 @default.
- W4387048790 hasRelatedWork W3013515612 @default.
- W4387048790 hasRelatedWork W3173596272 @default.
- W4387048790 hasRelatedWork W2187500075 @default.
- W4387048790 hasRelatedWork W2345184372 @default.
- W4387048790 isParatext "false" @default.
- W4387048790 isRetracted "false" @default.
- W4387048790 workType "book-chapter" @default.