FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3137356499> ?p ?o ?g. }

• W3137356499 endingPage "1148" @default.
• W3137356499 startingPage "1148" @default.
• W3137356499 abstract "High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research." @default.
• W3137356499 created "2021-03-29" @default.
• W3137356499 creator A5036614576 @default.
• W3137356499 creator A5044889784 @default.
• W3137356499 creator A5060576851 @default.
• W3137356499 creator A5084948520 @default.
• W3137356499 date "2021-03-17" @default.
• W3137356499 modified "2023-10-02" @default.
• W3137356499 title "High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images" @default.
• W3137356499 cites W1525224174 @default.
• W3137356499 cites W1903469757 @default.
• W3137356499 cites W2045804185 @default.
• W3137356499 cites W2082081125 @default.
• W3137356499 cites W2138973222 @default.
• W3137356499 cites W2261059368 @default.
• W3137356499 cites W2332208325 @default.
• W3137356499 cites W2525592260 @default.
• W3137356499 cites W2560266092 @default.
• W3137356499 cites W2584311710 @default.
• W3137356499 cites W2592849532 @default.
• W3137356499 cites W2612144337 @default.
• W3137356499 cites W2736681571 @default.
• W3137356499 cites W2757637497 @default.
• W3137356499 cites W2767953525 @default.
• W3137356499 cites W2771045332 @default.
• W3137356499 cites W2790898247 @default.
• W3137356499 cites W2806865914 @default.
• W3137356499 cites W2883925605 @default.
• W3137356499 cites W2886106861 @default.
• W3137356499 cites W2911964244 @default.
• W3137356499 cites W2920560278 @default.
• W3137356499 cites W2938610884 @default.
• W3137356499 cites W2951274592 @default.
• W3137356499 cites W2964421288 @default.
• W3137356499 cites W2990392801 @default.
• W3137356499 cites W2997443015 @default.
• W3137356499 cites W3013386193 @default.
• W3137356499 cites W3025172026 @default.
• W3137356499 cites W3032995909 @default.
• W3137356499 cites W3091952054 @default.
• W3137356499 cites W3106378566 @default.
• W3137356499 doi "https://doi.org/10.3390/rs13061148" @default.
• W3137356499 hasPublicationYear "2021" @default.
• W3137356499 type Work @default.
• W3137356499 sameAs 3137356499 @default.
• W3137356499 citedByCount "18" @default.
• W3137356499 countsByYear W31373564992021 @default.
• W3137356499 countsByYear W31373564992022 @default.
• W3137356499 countsByYear W31373564992023 @default.
• W3137356499 crossrefType "journal-article" @default.
• W3137356499 hasAuthorship W3137356499A5036614576 @default.
• W3137356499 hasAuthorship W3137356499A5044889784 @default.
• W3137356499 hasAuthorship W3137356499A5060576851 @default.
• W3137356499 hasAuthorship W3137356499A5084948520 @default.
• W3137356499 hasBestOaLocation W31373564991 @default.
• W3137356499 hasConcept C127313418 @default.
• W3137356499 hasConcept C154945302 @default.
• W3137356499 hasConcept C173163844 @default.
• W3137356499 hasConcept C205372480 @default.
• W3137356499 hasConcept C41008148 @default.
• W3137356499 hasConcept C62649853 @default.
• W3137356499 hasConcept C87360688 @default.
• W3137356499 hasConcept C89600930 @default.
• W3137356499 hasConceptScore W3137356499C127313418 @default.
• W3137356499 hasConceptScore W3137356499C154945302 @default.
• W3137356499 hasConceptScore W3137356499C173163844 @default.
• W3137356499 hasConceptScore W3137356499C205372480 @default.
• W3137356499 hasConceptScore W3137356499C41008148 @default.
• W3137356499 hasConceptScore W3137356499C62649853 @default.
• W3137356499 hasConceptScore W3137356499C87360688 @default.
• W3137356499 hasConceptScore W3137356499C89600930 @default.
• W3137356499 hasIssue "6" @default.
• W3137356499 hasLocation W31373564991 @default.
• W3137356499 hasOpenAccess W3137356499 @default.
• W3137356499 hasPrimaryLocation W31373564991 @default.
• W3137356499 hasRelatedWork W2019714000 @default.
• W3137356499 hasRelatedWork W2021348799 @default.
• W3137356499 hasRelatedWork W2025039112 @default.
• W3137356499 hasRelatedWork W2046570986 @default.
• W3137356499 hasRelatedWork W2133125644 @default.
• W3137356499 hasRelatedWork W2540644541 @default.
• W3137356499 hasRelatedWork W2776398399 @default.
• W3137356499 hasRelatedWork W2905390890 @default.
• W3137356499 hasRelatedWork W3025736352 @default.
• W3137356499 hasRelatedWork W3121488101 @default.
• W3137356499 hasVolume "13" @default.
• W3137356499 isParatext "false" @default.
• W3137356499 isRetracted "false" @default.
• W3137356499 magId "3137356499" @default.
• W3137356499 workType "article" @default.