Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation
By: Mustafa Ustuner
Tags: Machine Learning, Remote Sensing, Lightgbm, Scikit Learn, Synthetic Aperture Radar

1. Introduction

The accurate and systematic monitoring of croplands at the regional and global scale is crucial for many agricultural applications, ranging from the monitoring of crop productivity and biomass estimation, to managing agricultural subsidies. Due to the dynamic structure of crops, timely updates of the crop maps are needed for crop yield forecasting and the planning of agricultural policies [1,2]. Due to its temporal and broad-coverage monitoring capabilities, remote sensing offers practical solutions to these challenges. To oversee the spatial and temporal changes of crops, as well as for accurate crop identification, the classification of multi-temporal images is a common approach, providing a cost-effective solutions as compared to traditional techniques, such as land surveying or cadastral surveying for mapping [3,4,5]. The unavailability of cloud-free images from optical sensors can restrict the temporal monitoring of agricultural lands in some particular regions (e.g., tropical regions). Cloud coverage and haze could be a problem for large-scale agricultural applications, especially during the growing (or vegetation) season of crops. In such cases, Synthetic Aperture Radar (SAR) images are essential, which provide high temporal coverage in time-critical agricultural applications [1,4,6]. The requirements for multi-temporal and multi-polarization SAR data for successful crop classification have been clearly stated and verified in many previous research papers, due to crops overall, as well as within various phenological stages, demonstrating different scattering mechanisms in the temporal domain [1,3,4].

SAR signals are sensitive to crop geometric structure and dielectric properties, and hence they could provide complementary information to the optical sensors for crop analysis and monitoring. The radar backscatter, after calibration, is highly dependent upon the relationship between the target parameters (plant density, surface roughness, geometric structure, dielectric properties, etc.) and sensor configuration (incidence angle, polarization of wave, wavelength, etc.) [7,8,9]. When compared to single or dual-polarization modes, full polarimetric SAR (PolSAR) sensors record the complete scattering characteristics of the targets (i.e., vegetation or crops) and enable the analysis of the independent elements of physical scattering [7,10]. These complete scattering mechanisms can be analyzed and simplified using polarimetric target decomposition techniques. Polarimetric target decompositions are a powerful approach for the interpretation and analysis of complex scattering mechanisms, since they separate the polarimetric measurements (covariance/coherency matrices) into several independent secondary elements, and then provide significant information regarding various scattering mechanisms. Polarimetric target decompositions can be grouped into two categories: (i) coherent decompositions and (ii) incoherent decompositions [7,11,12]. Coherent decompositions deal with the single look scattering matrix S, and can characterize the coherent or deterministic scatterer (i.e., man-made targets). In practice, targets that are observed by the SAR sensors have different scattering characteristics, known as distributed or incoherent scatterers (i.e., natural targets). A distributed scatterer can only be characterized or modelled by using second-order statistics that can be obtained by either coherency T or covariance C matrices. Incoherent decompositions, which utilize T or C matrices, are applied in order to analyze the complex scattering mechanisms of incoherent scatterers, and they are divided into two categories: eigenvector based decompositions (e.g., Cloude–Pottier and Van Zyl) and model based decompositions (e.g., Freeman–Durden and Yamaguchi) [11,12,13,14].

The ability of polarimetric target decompositions to classify land use/cover has been investigated in several studies in the past [15,16,17]. However, only few studies have investigated radar-only crop-dominated classification by exploiting polarimetric target decompositions from multi-temporal C-band polarimetric RADARSAT-2 imagery. For this purpose, a wealth of classification algorithms has been utilized, such as support vector machines (SVM), maximum likelihood classification (MLC), random forest (RF), and nearest neighbor (NN). Ma et al. [18] used the polarimetric parameters of Pauli decomposition for crop classification to assess the potential of multi-temporal RADARSAT-2 PolSAR data. They achieved an overall classification accuracy of 87.8% while using MLC. Liu et al. [19] tested the multi-temporal RADARSAT-2 PolSAR images for the monitoring of crop growth. In order to analyze the scattering characteristics of crops, they exploited Pauli decomposition and used MLC for crop mapping. They reached up to 85% classification accuracy. Jiao et al. [4] investigated the Cloude–Pottier and Freeman–Durden decomposition methods for crop mapping, using object-oriented classification and multi-temporal RADARSAT-2 PolSAR data. Their experimental results demonstrated that the Cloude–Pottier decomposition parameters achieved higher classification accuracy than the Freeman–Durden decompositions and linear backscatter polarizations. Larrañaga and Álvarez-Mozos [2] evaluated the Pauli and Cloude–Pottier decompositions, as well as other several polarimetric features for crop mapping, with object based RF classification. They pointed out that C-band dual pol data could be sufficient if the dates of the images represent the key phenological changes of the crops. Shuai et al. [20] proposed a novel method for maize mapping using a parcel-based SAR classification with SVM. In their experiment, they extracted the polarimetric features from Pauli and Cloude–Pottier decompositions. Their experimental research concluded that their proposed method obtained a classification accuracy of 94.7%, and proved the added benefit of multi-temporal PolSAR data for maize mapping.

These aforementioned studies explored the potential of coherent and incoherent polarimetric target decompositions in crop classification while using multi-temporal C-band PolSAR data. However, the inter-comparison of incoherent decompositions (eigenvector based decompositions versus model based decompositions) in discriminating crop types from multi-temporal C-band PolSAR data has yet to be fully explored in the literature. Jiao et al. [4] used only one method (Freeman–Durden) from the model-based decompositions in their experiment. Furthermore, none of these above studies compared the capabilities of the C or T matrices alongside polarimetric target decompositions in crop classification using multi-temporal PolSAR data.

Over the last two decades, an ensemble of classifiers (multiple classifiers) have been widely exploited in the field of pattern recognition and machine learning for classification/regression problems, as ensemble classifiers are believed to attain higher accuracy when compared to single classifiers [21,22]. RF is the most widely used and recognized ensemble learning method in remote sensing due to its high accuracy, short computation time, and notable efficiency [22]. Recently, new ensemble learning algorithms, such as canonical correlation forest (CCF) (2015), extreme gradient boosting (XgBoost) (2016), and Light Gradient Boosting Machine (LightGBM) (2017), have been introduced to the machine learning community [23,24,25]. A very limited number of papers have been published regarding CCF [26,27] and XgBoost [28,29,30] for classification purposes in remote sensing; however, no study has been published yet using the recently launched LightGBM, which is a highly efficient gradient boosting decision tree that was developed by Microsoft Research in the field of remote sensing for classification purposes. Only in one paper by Liu, Ji, and Buchroithner [31] has LightGBM been tested, in this case for soil property retrieval by combining partial least squares. To the best of our knowledge, this experimental study is the first implementation of LightGBM for crop classification while using PolSAR data.

The main contributions of this research can be summarized, as follows:

(1) The performances of incoherent polarimetric target decompositions (eigenvector-based versus model-based) for discriminating crops using multi-temporal C-band PolSAR data were compared.

(2) For the first time, LightGBM was used for crop classification using the polarimetric features of multi-temporal C-band PolSAR data.

(3) The performance of polarimetric target decomposed parameters for crop classification was compared with the original polarimetric features (linear backscatter coefficients, and T and C matrices).

2. Light Gradient Boosting Machine

Due to their superiority to single classifiers in terms of classification performance, over the last few years ensemble classifiers (also known as multiple classifiers) have received greater attention in pattern recognition and machine learning. These classifiers aim to improve classification accuracy by combining the predictions of individual (single) classifiers through the majority voting rule [42,43]. There are several approaches that are used for generating ensemble classifiers, such as bagging, boosting, stacking, and RF [16]. Since we only implemented LightGBM as an ensemble method, we will only provide the basic principles of how boosting works. In the boosting approach, a series of individual classifiers are iteratively trained to improve the weak learner classifiers. In the initial phase of the iteration, the training data are equally weighted, and during the training process, the weights are re-calculated. The misclassified pixels, due to weak classifiers in the previous iterations, are assigned to stronger weights and are thus correctly classified [21,42]. One of the most common boosting methods in the field of remote sensing and machine learning is gradient-boosted decision trees (GBDT), which is also known as gradient-boosting machines (GBM).

Recently, new ensemble learning algorithms, such as CCF (2015) and XgBoost (2016), have been explored and tested for the classification of remotely sensed images in a number of studies [26,27,28,29]. A relatively new ensemble learning algorithm, LightGBM (2017), has been introduced to the machine learning community, and has been received with great interest because it has outperformed the existing boosting frameworks in recent machine learning and data science competitions, especially in regards to complex datasets [43]. A brief introduction of LightGBM is provided in the following paragraph; more details can be read in Ke et al.’s work [25]. LightGBM classifications were performed using the LightGBM Python Package v.2.2.2 [44]

LightGBM is a gradient boosting framework that is based on decision tree algorithms. This method grows the trees by applying the leaf-wise (or best-first) strategy, while other ensemble learning algorithms use the level-wise (or depth-wise) strategy. LightGBM benefits from two novel techniques, called Gradient-Based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). The computation costs are reduced through these novel techniques, since GOSS uses a subset of smaller instances instead of all instances, and EFB can bundle exclusive features into less dense features. These techniques in LightGBM provide advantages, such as accelerating the training process and obtaining higher accuracy than other gradient boosting frameworks. This method is called “LightGBM” due to the high speed training process and low memory usage. LightGBM has many model parameters that need to be tuned, such as boosting type, max depth, learning rate, and number of leaves. As a boosting type, we chose GOSS [25,31]. The parameters were tuned by the trial and error method, and they are listed in Table 5.

Light Gradient Boosting Machine (LightGBM) parameters

4. Experimental Results and Discussion

In this subsection, the classification results (and class based accuracies) are presented, and the impacts of polarimetric target decomposition are then discussed. The inter-comparison of the incoherent polarimetric target decomposition methods, along with the original PolSAR features for crop classification, was performed in this study by using the LightGBM ensemble learning classifier. The accuracies of the classified images (and class predictions) were derived from the error matrix (confusion matrix). Figure 4 displays the overall classification accuracies. The highest classification accuracy was obtained by the Yamaguchi decomposition method (92.07%), followed by the Van Zyl (91.76%) and Freeman–Durden (91.60%) decompositions. All of the polarimetric target decomposition methods, with the exception of Cloude–Pottier (78.83%), outperformed the backscatter coefficients (87.65%) in terms of overall classification accuracy. The model based decompositions (Yamaguchi and Freeman-Durden) outperformed the eigenvector based decompositions, with the exception of the Van Zyl method. Even though the Cloude–Pottier resulted in a lower classification accuracy than the backscatter coefficient and other polarimetric target decompositions, it obtained a higher classification accuracy than the T matrix (76.47%) and C matrix (75.99%). Furthermore, when comparing the classification performances of the T and C matrices, the T matrix outperformed the C matrix. The possible reason for this is because the T matrix includes information regarding the physical scattering mechanism, however the C matrix includes only the second order formalism of the scattering matrix and allows for the direct analysis of polarization channels [39].

Classification accuracies (overall accuracies).

This research was originally published in ISPRS International Journal of Geo-Information (ISSN 2220–9964)

Find out more at https://www.mdpi.com/2220-9964/8/2/97

Cite this: Ustuner, M.; Balik Sanli, F. Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation. ISPRS Int. J. Geo-Inf. 2019, 8, 97.

References