Evaluation of the discrimination capability of full polarimetric SAR data for crop classification

Zeyada, Hind H.; Ezz, Mohamed; Nasr, Ayman H.; Shokr, Mohammed; Harb, Hany M.

doi:10.1080/01431161.2016.1182663

Cited by 27 publications

(7 citation statements)

References 21 publications

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“…It has been reported that VH polarization is more sensitive to the structural and geometrical arrangements of plants [11,38]. This is in accordance with the result that VH intensity bands held the biggest proportion in the selected SAR features (also in all features) for crop type discrimination (Figure 14b).…”

Section: The Contribution Of Sar Features In Crop Type Discriminationsupporting

confidence: 89%

“…As synthetic aperture radar (SAR) can reflect the structure of vegetation, and optical imagery captures the multi-spectral information of crops, it has been indicated that the synergetic use of SAR and optical data can be complementary to each other [8,9].Space-borne SAR, due to its all-day, all-weather capability, wide coverage, and strong penetrating ability, has been increasingly used in crop classification, to complement with the use of optical imagery. It was found that considerable improvement can be achieved by increasing polarization channels [10,11]. Some studies suggested that using multi-temporal acquisitions can improve the accuracy of crop type mapping, and cross-polarized backscatter outperforms other polarization modes [12,13].…”

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confidence: 99%

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Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

et al. 2020

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Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

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Section: The Contribution Of Sar Features In Crop Type Discriminationsupporting

confidence: 89%

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confidence: 99%

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

et al. 2020

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“…The contribution of polarimetry was also assessed in Reference [33] by testing the performance when input information is removed progressively. The capability of polarimetric SAR data in crop classification has also been demonstrated by applying machine learning algorithms such as Random Forest (RF) [34][35][36], Decision Tree (DT) [37,38], Neural Networks (NNs) [39] and Support Vector Machines (SVMs) [40,41].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

et al. 2019

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Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).

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“…As the scientific field of Radar Polarimetry progresses, many polarimetric decomposition techniques have been proposed for quantitatively analyzing PolSAR data and extracting information about the scattering characteristics of Earth's objects [33], and many of them have been successfully employed in crop classification studies [34]. H/A/α decomposition method, conceptualized by Cloude and Pottier (1997) [35], is one of the most popular among them [36] and it is based on the eigendecomposition of the multilooked polarimetric coherency or covariance matrix. This method was initially developed for the analysis of quad-pol data and it was later adapted for the dual-pol data case [37].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

et al. 2022

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Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.

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Evaluation of the discrimination capability of full polarimetric SAR data for crop classification

Cited by 27 publications

References 21 publications

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

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