FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3089655035> ?p ?o ?g. }

• W3089655035 endingPage "e0239746" @default.
• W3089655035 startingPage "e0239746" @default.
• W3089655035 abstract "This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%." @default.
• W3089655035 created "2020-10-08" @default.
• W3089655035 creator A5014580983 @default.
• W3089655035 creator A5031630129 @default.
• W3089655035 creator A5053314319 @default.
• W3089655035 creator A5053691175 @default.
• W3089655035 creator A5088373701 @default.
• W3089655035 creator A5089263595 @default.
• W3089655035 date "2020-09-28" @default.
• W3089655035 modified "2023-10-16" @default.
• W3089655035 title "On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network" @default.
• W3089655035 cites W199094806 @default.
• W3089655035 cites W2092547149 @default.
• W3089655035 cites W2097117768 @default.
• W3089655035 cites W2132424470 @default.
• W3089655035 cites W2472919595 @default.
• W3089655035 cites W2604086375 @default.
• W3089655035 cites W2620779710 @default.
• W3089655035 cites W2741754853 @default.
• W3089655035 cites W2742878349 @default.
• W3089655035 cites W2782522152 @default.
• W3089655035 cites W2890766736 @default.
• W3089655035 cites W2892035503 @default.
• W3089655035 cites W2903282641 @default.
• W3089655035 cites W2906799152 @default.
• W3089655035 cites W2908749843 @default.
• W3089655035 cites W2939880928 @default.
• W3089655035 cites W2940726923 @default.
• W3089655035 cites W2963131120 @default.
• W3089655035 cites W3031245058 @default.
• W3089655035 cites W3034847898 @default.
• W3089655035 doi "https://doi.org/10.1371/journal.pone.0239746" @default.
• W3089655035 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7521744" @default.
• W3089655035 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32986785" @default.
• W3089655035 hasPublicationYear "2020" @default.
• W3089655035 type Work @default.
• W3089655035 sameAs 3089655035 @default.
• W3089655035 citedByCount "14" @default.
• W3089655035 countsByYear W30896550352021 @default.
• W3089655035 countsByYear W30896550352022 @default.
• W3089655035 countsByYear W30896550352023 @default.
• W3089655035 crossrefType "journal-article" @default.
• W3089655035 hasAuthorship W3089655035A5014580983 @default.
• W3089655035 hasAuthorship W3089655035A5031630129 @default.
• W3089655035 hasAuthorship W3089655035A5053314319 @default.
• W3089655035 hasAuthorship W3089655035A5053691175 @default.
• W3089655035 hasAuthorship W3089655035A5088373701 @default.
• W3089655035 hasAuthorship W3089655035A5089263595 @default.
• W3089655035 hasBestOaLocation W30896550351 @default.
• W3089655035 hasConcept C108583219 @default.
• W3089655035 hasConcept C115961682 @default.
• W3089655035 hasConcept C119857082 @default.
• W3089655035 hasConcept C146849305 @default.
• W3089655035 hasConcept C154945302 @default.
• W3089655035 hasConcept C188441871 @default.
• W3089655035 hasConcept C205649164 @default.
• W3089655035 hasConcept C2778102629 @default.
• W3089655035 hasConcept C41008148 @default.
• W3089655035 hasConcept C50644808 @default.
• W3089655035 hasConcept C62649853 @default.
• W3089655035 hasConcept C75294576 @default.
• W3089655035 hasConcept C81363708 @default.
• W3089655035 hasConceptScore W3089655035C108583219 @default.
• W3089655035 hasConceptScore W3089655035C115961682 @default.
• W3089655035 hasConceptScore W3089655035C119857082 @default.
• W3089655035 hasConceptScore W3089655035C146849305 @default.
• W3089655035 hasConceptScore W3089655035C154945302 @default.
• W3089655035 hasConceptScore W3089655035C188441871 @default.
• W3089655035 hasConceptScore W3089655035C205649164 @default.
• W3089655035 hasConceptScore W3089655035C2778102629 @default.
• W3089655035 hasConceptScore W3089655035C41008148 @default.
• W3089655035 hasConceptScore W3089655035C50644808 @default.
• W3089655035 hasConceptScore W3089655035C62649853 @default.
• W3089655035 hasConceptScore W3089655035C75294576 @default.
• W3089655035 hasConceptScore W3089655035C81363708 @default.
• W3089655035 hasIssue "9" @default.
• W3089655035 hasLocation W30896550351 @default.
• W3089655035 hasLocation W30896550352 @default.
• W3089655035 hasLocation W30896550353 @default.
• W3089655035 hasOpenAccess W3089655035 @default.
• W3089655035 hasPrimaryLocation W30896550351 @default.
• W3089655035 hasRelatedWork W2770149305 @default.
• W3089655035 hasRelatedWork W2899027234 @default.
• W3089655035 hasRelatedWork W2911497689 @default.
• W3089655035 hasRelatedWork W2952813363 @default.
• W3089655035 hasRelatedWork W3176438653 @default.
• W3089655035 hasRelatedWork W4220732972 @default.
• W3089655035 hasRelatedWork W4229443568 @default.
• W3089655035 hasRelatedWork W4360783045 @default.
• W3089655035 hasRelatedWork W4378678253 @default.
• W3089655035 hasRelatedWork W292456094 @default.
• W3089655035 hasVolume "15" @default.
• W3089655035 isParatext "false" @default.
• W3089655035 isRetracted "false" @default.
• W3089655035 magId "3089655035" @default.
• W3089655035 workType "article" @default.