In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Dey, Subhadip; Mandal, Dipankar; Robertson, Laura Dingle; Banerjee, Biplab; Kumar, Vineet; McNairn, Heather; Bhattacharya, Avik; Rao, Y. S.

doi:10.1016/j.jag.2020.102059

Cited by 32 publications

(13 citation statements)

References 24 publications

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“…They verified the outperformance of XGBoost method over the rest of the classifiers. In a study conducted by Dey et al, (2020), they used RF and XGBoost classifiers for crop-type mapping using full polarimetric Radarsat-2 PolSAR satellite images [27]. Based on their assessment in two different case studies, the accuracy of XGBoost is strongly higher than RF.…”

Section: Bagging Boostingmentioning

confidence: 99%

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

et al. 2021

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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.

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Section: Bagging Boostingmentioning

confidence: 99%

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

et al. 2021

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“…Rice is grown in two distinct seasons: monsoon or kharif (June-November) and winter or rabi (December-March). The test site is a super site, where numerous techniques have been tested to characterize crops [28], classification assessments [29], and vegetation condition monitoring by means of derived radar vegetation indices [30] using C-band SAR data in full and compact-pol modes. The data used in this study were limited to rice cultivation during the kharif season of 2014 and 2018.…”

Section: Indiamentioning

confidence: 99%

A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

et al. 2021

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The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2 m−2 and mean absolute error (MAE) of 0.51 m2 m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2 m−2 and MAE of 0.61 m2 m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2 m−2 and MAE of 0.30 m2 m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.

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“…Before the launch of Sentinel-1, a number of research works have been carried out to use satellite radar images for crop classification, with different bands of acquisition-Lband [24,25], C-band [24,26,27] (and different polarizations), VV, HH, and HV [27]. The C-band Sentinel-1 SAR data have been analyzed temporally to recognize which agricultural crops grow in fields [28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring

et al. 2021

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In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher.

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In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Cited by 32 publications

References 24 publications

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring

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