Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

Abstract

The classification maps are required for management and for the estimation of agricultural disaster compensation; however, those techniques have yet to be established. Some supervised learning models may allow accurate classification. In this study, the Random Forest (RF) classifier and the classification and regression tree (CART) were applied to evaluate the potential of multi-temporal TerraSAR-X dual-polarimetric data, on the StripMap mode, for classification of crop type. Furthermore, comparisons of the two algorithms and polarizations were carried out. In the study area, beans, beet, grasslands, maize, potato and winter wheat were cultivated, and these crop types were classified using the data set acquired in 2009. The classification results of RF were superior to those of CART and the overall accuracies were 0.91 to 0.93.

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

“…Therefore, it will be very helpful to develop a method and corresponding computer programs to search these optimal thresholds automatically. Though the decision tree classifier achieved higher accuracy than the maximum likelihood classifier in this study, support vector machine (SVM) [14], random forest [46] and other machine learning classifiers [47,48] can be also tested to get an optimal combination of the main classification processes in identifying maize.…”

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

“…A number of authors have also achieved comparable or improved results with Random Forest, compared to other non-parametric approaches, including Classification and Regression Trees (CARTs) [23,27,30,34], Support Vector Machines [29,[35][36][37], and Neural Networks [29]. These approaches typically require more user-interference with classifier settings whereas Random Forest only requires that users define: (1) the number of trees that are generated; and (2) the number of variables tested during each iteration of node splitting (described subsequently); a benefit that is commonly noted in the literature [24,38,39].…”

“…To determine the split at each node, a random subset of available predictor variables are tested, and only that variable which provides the best split is used [38,41]. This approach seeks to reduce the degree of correlation amongst individual trees in the forest, which often improves performance and enables the use of both independent and dependent data [14,27,30,31,34]. The model is also said to be robust to overfitting, and since only a subset of all variables are used to determine the split at each node, the algorithm is more computationally efficient than other methods (e.g., boosting), which better permits the use of highly dimensional datasets [27,30,31].…”

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

“…More recently, with the launch of the TerraSAR-X and COSMO-SkyMed satellites, the use of X-band SAR data has largely expanded, mainly thanks to the higher spatial and temporal resolutions and theoretical flexibility of these platforms [35,36]. Concerning X-band SAR data, different polarimetric configurations have been tested for crop mapping, from vertical-based, e.g., in [37], to horizontal-based polarization, e.g., in [38]; comparative studies using multiple polarizations have been carried out as well, e.g., in [39]. However, to our knowledge no agreement has been reached so far on the best polarization configuration for crop mapping.…”