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RISS 인기검색어
- 검색키워드
- 저자 : Haldar Dipanwita (검색결과 2 건)
- 무료
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Selvaraj Shanmugapriya,Haldar Dipanwita,Srivastava Hari Shanker 대한공간정보학회 2021 Spatial Information Research Vol.29 No.5
The study was focused on assessing the condition of pearl millet crop in critical growth stages using both polarimetric Radarsat-2 and dual-polarized Sentinel-1 datasets. The results revealed that bajra having a close structured phenology like maize and Jowar, exhibited significant changes in RVI due to differences in the crop calendar dates. For bajra, polarimetric RVI generated from information rich Radarsat-2 was observed to have a higher level of saturation till 6 kgm-2 biomass with a R2 of 0.7. In all instances, RVI exhibited a significant relationship with VWC and plant volume with a R2 above 0.7 due to its higher sensitivity towards crop dielectric constant. Unlike NDVI, RVI increased with an increase in Leaf Area Index till 5.8 even during panicle initiation stage. Backscatter and truncated RVI almost follow a similar trend of RVI response for various crop growth parameters. Hence, in case of regional analysis and high cost of Radarsat-2 dataset, one can use freely available sentinel data for RVI analysis due to its wider swath coverage. The observed high correlation of crop age with RVI (R2 = 0.6) proved to be the best tool for predicting sowing dates in staggered sowing zones.
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Evaluation of machine learning algorithms to Sentinel SAR data
Ashish Navale,Dipanwita Haldar 대한공간정보학회 2020 Spatial Information Research Vol.28 No.3
The present study uses multi-temporal Sentinel- 1 SAR dataset for classification of Saharanpur area in the Indo-Gangetic plains with December, January and February month datasets of VV and VV/VH polarization. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT) and Artificial Neural Network (ANN) algorithms with six different band combinations was used to classify the data in 6 classes. The highest accuracy was achieved with SVM for December–January combination with Overall Accuracy of 74.36% and a kappa coefficient of 0.6905. SVM algorithm performed the best followed by DT, ANN and RF. It was observed that the accuracy of classification increased with multi-temporal datasets. In SVM and RF the accuracy increased by almost 8% from single to dual date, but no increase in accuracy was observed irrespective of taking three dates. For DT and ANN, the accuracy from single to dual date increased by[10% and by approximately 3% (Marginal) for three dates. The single date ANN achieved very poor results but with an increase in the datasets, good accuracy was attained. This study, therefore, reveals that with single and dual datasets, SVM and RF performs well and with multitemporal datasets, DT and ANN can also achieve good accuracy.
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