Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
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In this article, the authors evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data and found that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) as the classifier with the highest accuracy among all those tested.Abstract:
The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.read more
Citations
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Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
Maria Ioannidou,Alkiviadis-Marios Koukos,Vasileios Sitokonstantinou,Ioannis Papoutsis,Charalampos Kontoes +4 more
TL;DR: In this article , the authors applied the Cloude-Pottier polarimetric decomposition on PolSAR data and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features.
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From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2
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TL;DR: Wang et al. as mentioned in this paper proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images.
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