Effect of Red-Edge Region in Fuzzy Classification: A Case Study of Sunflower Crop

Vincent, Asha; Kumar, Anil; Upadhyay, Priyadarshi

doi:10.1007/s12524-020-01109-4

Cited by 12 publications

(13 citation statements)

References 12 publications

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“…Initially, various vegetation indices were applied on the dataset to reduce the spectral dimensionality while retaining the temporal dimensionality. Different band combinations considered for generating those indices are maximum and minimum reflectance bands for CBSI-MSAVI2, NIR and Red for MSAVI2 [41] and NIR and Red Edge1 band to generate MSAVI2RE1 [42].…”

Section: Methodology Adoptedmentioning

confidence: 99%

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Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

et al. 2022

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This research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approach.

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Section: Methodology Adoptedmentioning

confidence: 99%

“…The formula used to calculate CBSI-MSAVI2 is mentioned in Equation (7), where ρ max and ρ min denotes maximum and minimum reflectance values. The bands NIR and Red in the calculation of MSAVI2 [41] have been replaced with ρ max and ρ min .…”

Section: Cbsi-msavi2 Indicesmentioning

confidence: 99%

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

et al. 2022

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“…The analysis showed the importance of VV and VH polarization and SWIR bands' data for the classification. Vincent et al [175] also obtained positive effects optimizing the temporal date images to map sunflower fields.…”

Section: Incorporating Radar Datamentioning

confidence: 97%

“…The inclusion of Red-edge bands improved all class-specific accuracies. Vincent et al [175] attested the importance of the S2/MSI Red-edge bands and derived VIs (NDVIre, SAVIre, MSRre, and CIre) to map sunflower and wheat crops in India, using a c-means classification approach. Sun et al [85] mapped six LULC classes at one level and five crop types at another level using an RF classifier and showed that including Red-edge, the OA of crop type mapping improved when compared with only conventional optical features.…”

Section: Less Frequently Used Spectral Bands and Vismentioning

confidence: 99%

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

2020

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Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

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“…The Sentinel-2 red-edge bands are important in agricultural applications [40], and the position of the red edge is an important index that can be used to measure the chlorophyll content of leaves. Using the reflectance of the red-edge region to calculate a vegetation index can thus improve the classification accuracy [41]. Therefore, a red edge normalization index (RENDVI) and a red edge position index (REP) were also constructed by using the red-edge bands of Sentinel-2 data [42].…”

Section: Feature Constructionmentioning

confidence: 99%

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

et al. 2022

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The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding‒tillering, overwintering, reviving, jointing‒heading, and flowering‒maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries‒Matusita (J‒M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing‒heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing‒heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing‒heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation.

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Effect of Red-Edge Region in Fuzzy Classification: A Case Study of Sunflower Crop

Cited by 12 publications

References 12 publications

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

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