1. Introduction
The extent of a cultivated area is an important factor in estimating crop harvest. Crop maps, documenting field properties, including crop type and location, have been generated by some local governments in Japan. However, the documentation of field properties is generally done manually, and the development of easier methods, such as techniques based on satellite remote sensing, is required due to the high cost of existing methods [
1].
Optical remote sensing is one of the most attractive options; in particular, Landsat series data have potential in land characterization applications due to their spatial, spectral, and radiometric qualities [
2,
3,
4,
5]. Furthermore, the Sentinel-2 satellites have contributed to create greater opportunities for monitoring plant constituents, such as pigments, leaf water contents, and biochemicals [
6,
7], and the vegetation indices calculated from Sentinel-2 Multispectral Instrument (MSI) data were useful to identify the specific crop types [
8,
9]. However, optical data are influenced by atmospheric or weather conditions, and the number of available scenes may be restricted. Data from synthetic aperture radar (SAR) systems are an alternative, offering a significant amount of information related to plant phenology, soil moisture, which affects the timing of seeding, transplanting, and harvesting, and vegetation parameters, such as crop height or crop cover rate [
10,
11,
12,
13]. SARs are not subject to atmospheric influences or weather conditions, making them suitable in multi-temporal classification approaches [
14,
15]. Previous studies have shown the potential of C and L-band SAR data to discriminate crop types [
16,
17], and a significant improvement in classification accuracy has been reported when SAR data and a Landsat8 satellite image time series were integrated [
2,
18]. Sentinel-1 data have been applied for mapping crop fields with the support of Sentinel-2 data or Landsat-8 data [
19,
20,
21,
22]. Besides these, TerraSAR-X and TanDEM-X provide X-band SAR data of high geometric accuracy at a high spatial resolution of 2.5–6 m in 30-km swaths in Stripmap mode [
23]. X-band SAR data are now widely available. The backscattering coefficient calculated from SAR data is a function of the geometry and dielectric properties of the target and the amount of biomass in agricultural fields [
24]. Therefore, temporal changes can be distinguished using multi-temporal SAR data. The major change in backscatter intensity occurs as a result of ploughing and seeding; smaller changes occur due to variations in the biomass and plant water content, and, for X-band SAR data, in plant structure. Harvesting also causes large backscatter intensity changes [
25]. At times, however, no change in backscatter intensity is observed despite geometric changes; this is typically observed for dense vegetation, such as grasslands, and for high-frequency SAR data, such as C-band data [
25], and this feature could help us to discriminate some specific crop types, such as grasslands and wheat fields.
Polarimetric decomposition is a promising technique for resolving this problem. In particular, quad-polarimetric observations provide more information than a single polarization and show potential for monitoring and mapping various crops. Several polarimetric decomposition methods have been developed to obtain more information about scattering. However, the doubled pulse repetition frequency leads to a reduced swath width when scanning all polarizations, which limits the opportunities to obtain quad-polarization SAR data. To overcome these problems, compact polarimetric techniques, such as m-chi decomposition [
26,
27] and dual polarization entropy/alpha decomposition [
28], have been proposed. In this study, we examined crop classifications using the polarimetric parameters obtained from the TerraSAR-X dual-polarimetric data (HH and VV polarization).
The radar vegetation index (RVI) is another technique that enhances the characteristic features of vegetation based on SAR data [
29,
30]. The original RVI is calculated from backscattering coefficients of HH, HV, and VV polarization; this index cannot be extracted from TerraSAR-X dual polarimetric data. However, based on the concept of vegetation indices (VIs) based on optical data that are calculated from simple formulas consisting of a combination of two or more reflectance wavebands, some indices can be calculated using gamma naught or m-chi decomposition parameters. In this study, indices based on differences (D-type index), simple ratios (SRs), and normalized differences (NDs), as well as combinations of these, were considered.
In addition to effective predictors, some supervised learning models may allow for accurate classification; however, different classification algorithms produce different results, even when the same training data are used [
31,
32]. Support vector machine (SVM) and random forests (RF) are the most effective classification approaches, and some previous studies have shown the strong potential of these techniques for identification of vegetation or soil types using remote-sensing data [
33,
34]. Extreme learning machines (ELMs) [
35] have also exhibited strong performances in terms of classification and regression, and kernel-based ELMs (KELMs) may offer the highest accuracy [
32,
36,
37,
38]. In addition to these techniques, recent major advances in Deep Learning Neural Networks are making it possible to solve problems that have resisted the best attempts of the artificial intelligence community [
39,
40]. Deep learning has a powerful learning capability feature that has been applied to optical remote-sensing data. Other recent techniques based on machine learning are multiple kernel methods, which consist of a convex combination of kernels, with the weight of each kernel optimized during training, and the multiple kernel learning (MKL) [
41,
42] and multiple kernel extreme learning machines (MKELMs) have been proposed [
43]. Although some previous studies provided a comparison of results among various machine-learning algorithms [
32], imbalanced data may result in poor classification results, providing worse results than those reported in other studies. In the present study, the abilities of six classification algorithms to identify imbalanced data were evaluated and compared.
The main objectives of this paper are (1) to evaluate the potential of TerraSAR-X data with respect to crop type classification and (2) to identify which algorithms are most suitable for classification in the study area.
3. Results and Discussion
3.1. Acquired Data
The seasonal changes in the TerraSAR-X data are shown in
Figure 2. After 26 May 2013, the highest gamma naught values were confirmed for beetroot fields due to the rosette leaves, and persisted until the end of August, the end of the beetroot-growing season. Beans, maize, and squash continued to grow until the middle of August. As a result, the temporal changes in the predictors of these crops were similar.
Although a decrease in both gamma naught values was confirmed from 6 June 2013 to 17 June 2013 for wheat, increases were confirmed for other crops. The absorption of microwave radiation with growth was reported for wheat and barley fields [
66] since they can be assumed to represent aggregate polarization. This period was the peak of the wheat-growing season in this study area, and the plant density continued to increase. As a result, Odd, Dbl, Rnd and the gamma naught values decreased due to absorption, although volume scattering was the main scattering pattern associated with increased biomass. While grass also possessed low gamma naught values due to a similar structure to wheat, the grasslands were mainly composed of two types of grass (timothy and orchard grass), and various types of other vegetation, such as dandelion and goosefoot, were mixed in due to low weed control. Such conditions reduced the microwave absorption ability.
Peak gamma naught values occurred on June 17 for potato but later for most of the other crops. Pronounced furrow ridges (30–35 cm in height) were generated in potato fields between 6 June 2013 and 17 June 2013, and direct reflections from the ridges led to an increase in the simple scattering patterns. The increase in Rnd was almost the same as for other crops, and a high alpha angle occurred on 9 July 2013, which corresponds to the peak of the potato-growing season.
The gamma naught values for yam fields increased over the whole period. For yams, meshing was applied from June and led to an increase in backscattering that stabilized at the end of June; the next increase occurred after 20 July 2013 due to the high coverage rate.
3.2. Selected Indices Based on LDA
The selected predictors calculated from TerraSAR-X data for each round are listed in
Table 4. The numbers of predictors ranged from 8 to 11. Four predictors (γº
VV acquired on 26 May 2013 and 9 July 2013; Rnd acquired on 28 June 2013; and ND (Dbl, Rnd) acquired on 6 June 2013) were selected for all repetitions. Dbl acquired on 22 August 2013 was selected except for Round 6, and Entropy acquired on 11 August 2013 was selected except for Rounds 5 and 6. Some combinations of gamma naught values and Raney decomposition parameters were frequently selected, especially ND (γº
HH, Dbl) or ND(γº
VV, Dbl), except for Round 7. Therefore, the usage of radar vegetation indices was more effective than the use of only original backscattering coefficients or polarimetric parameters.
3.3. Accuracy Assessment
The accuracies of the crop classification results, based on 10 repetitions, are shown in
Table 5. All algorithms achieved an OA higher than 91.5 % and performed well in classifying the agricultural crops and MKL had the lowest overall AD+QD.
Identification of beetroot, grass, potatoes, and wheat was accurate for all algorithms, and their PA, UA, and F1 scores were higher than 0.9. By contrast, some squash and maize fields were misclassified as beans, and associated accuracies were relatively low since they exhibited similar trends for their predictors and the sample size of the bean fields was the largest. FNN and MKL were somewhat robust in identifying crops whose sample sizes were very small. However, the four other algorithms had PAs less than 0.5 for squash. When the small-sized fields (squash and yam) were ignored and the number of crop types was equal to six, KELM performed more effectively than CART, SVM, RF, and FNN [
32,
67]. However, the results showed that this algorithm was relatively weak with respect to the addition of small fields. Slight improvements were observed in the identification of squash when the multiple kernel version (MKELM) was applied, but the accuracy was not higher than that observed for SVM and MKL. MKELM also had a poor ability to identify yam fields compared with KELM.
3.4. Statistical Comparison
The McNemar’s test results were used to compare classification accuracies (
Table 6). The differences in classification results were significant among the six algorithms (
p < 0.05) and MKL, which possessed the lowest overall AD+QD, emerged as the best algorithm for crop classification in this area (
Figure 3).
3.5. Sensitivity Analysis
DSA was conducted to clarify which predictors contributed to crop identification within the classification models based on MKL (
Figure 4). Although γº
VV acquired on 9 July 2013 was the most important metric for identifying wheat fields, Rnd acquired on 28 June 2013 was the most important metric for identifying beans, beetroot, and grassland and the second most important metric for identifying squash, wheat, and yams. On 28 June 2013, which was the start of the growth period for most crops except wheat, the differences in vegetation structure directly influenced the strength of volume scattering from each crop type; the respective highest and lowest Rnd values were confirmed for beetroot, whose rosettes caused efficient backscattering, and wheat, whose structure resulted in microwave absorption. It was easy to observe the differences between crop types due to the different plant heights, except for the combination of maize and potato (
Figure 2g). Dbl acquired on 22 August 2013 was another effective m-chi decomposition parameter and was the most important variable to identify squash fields. The contribution of Entropy acquired on 11 August 2013, when beans reached their maximum height and entered the ripening period, and the very small differences in scattering patterns among soy, azuki, and kidney beans, were effective for bean field identification.
The contributions of some RVIs were also confirmed, and the importance of D(Dbl, Odd) acquired on 17 June 2013 was 11.9%, 7.7%, 8.6%, 7.2%, and 4.4% for identifying beans, potato, squash, wheat, and yam fields, respectively. Similarly, the values for ND (Dbl, Rnd) acquired on 6 June 2013 were 14.0% (beetroot) and 8.0% (maize field identification), that for ND (Dbl, Rnd) acquired on 28 June 2013 was 6.5% (for squash), and that for ND (γºHH, Dbl) acquired on 28 June 2013 was 6.4% for wheat.
3.6. Relationship between Field Area and Misclassified Fields
Figure 5 shows the relationship between field area and misclassified fields when MKL was applied. In total, 40.5% of the misclassified fields were less than 1 ha, 36.9% were 1–2 ha in area. Therefore, a limitation related to the area of fields could improve the reliability of the classification maps, which could then be particularly effective for identifying grasslands and maize fields. However, smaller fields should not be ignored and some problems related to the borders of fields remain to be resolved. Some studies have shown that the use of a few sets of optical data contributed to the improvement of classification accuracies [
32], and future research is planned to evaluate the degree of certainty related to the edges.
Although a few misclassified fields were confirmed to cover an area greater than 5 ha, many grass fields were misclassified since fewer cultivation control methods were employed, and numerous weeds were present in the grasslands.
4. Conclusions
Certain decomposition techniques were applied to TerraSAR-X dual-polarimetric data, and polarimetric parameters were used for crop classification as well as gamma naught values of HH and VV polarization. Furthermore, radar vegetation indices (RVIs), which were calculated from gamma naught values and m-chi decomposition parameters, were also considered. Linear discriminant analysis allowed for the selection of several well-performing RVIs, which can improve classification accuracies. Six types of machine-learning algorithms were tested. While each algorithm achieved an OA value higher than 91.5% and all performed well in classifying agricultural crops, significant differences in classification results were observed. In this study area, the sample sizes of squash and yams fields were very small, occupying ca. 1.2% of the total area; most algorithms failed to identify these crops, especially squash. Of the tested algorithms, multiple kernel learning performed best, achieving an F1 score of 0.62 for the identification of squash fields, as well as an overall accuracy of 92.1%. However, meshing was applied over the yam fields from June and wilting related to chemical treatments was conducted over the potato fields in the study area. Therefore, agricultural practices should be paid attention when the method proposed in this study is handled.