Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery

: Pixel-based (PB) and geographic-object-based (GEOBIA) classification approaches allow the extraction of different objects from multispectral images (MS). The primary goal of this research was the analysis of UAV imagery applicability and accuracy assessment of MLC and SVM classification algorithms within PB and GEOBIA classification approaches. The secondary goal was to use different accuracy assessment metrics to determine which of the two tested classification algorithms (SVM and MLC) most reliably distinguishes olive tree crowns and which approach is more accurate (PB or GEOBIA). The third goal was to add false polygon samples for Correctness (COR), Completeness (COM) and Overall Quality (OQ) metrics and use them to calculate the Total Accuracy (TA). The methodology can be divided into six steps, from data acquisition to selection of the best classification algorithm after accuracy assessment. High-quality DOP (digital orthophoto) and UAV MS were generated. A new accuracy metric, called Total Accuracy (TA), combined both false and true positive polygon samples, thus providing a more comprehensive insight into the assessed classification accuracy. The SVM (GEOBIA) was the most reliable classification algorithm for extracting olive tree crowns from UAV MS imagery. The assessment carried out indicated that application of GEOBIA-SVM achieved a TA COR of 0.527, TA COM of 0.811, TA OQ of 0.745, Overall Accuracy (OA) of 0.926 or 0.980 and Area Under Curve (AUC) value of 0.904 or 0.929. The calculated accuracy metrics confirmed that the GEOBIA approach (SVM and MLC) achieved more accurate olive tree crown extraction than the PB approach (SVM and MLC) if applied to classifying VHR UAV MS imagery. The SVM classification algorithm extracted olive tree crowns more accurately than MLC in both approaches. However, the accuracy assessment has proven that PB classification algorithms can also achieve satisfactory accuracy.


Introduction and Background
The olive is one of the oldest and most widely cultivated species in the Mediterranean [1][2][3]. Over the centuries, the distribution, spread, and finally the dominance of olives through the centuries have shaped the Mediterranean landscape's recognizable character [1]. Over 70% of the olives in the world are grown in the Mediterranean countries of the European Union [4]. Olives are well adapted to sloping and poorly fertile soils, thus providing an ecological, economic and social benefit to the areas in which they are cultivated [5]. Preservation of olive groves as an element of Mediterranean identity [6] and a strategic economic resource [7] depends on sustainable environmental management, The primary goal of this research is the analysis of UAV imagery applicability and accuracy assessment of the most commonly used classification algorithms (MLC and SVM) [34,[36][37][38][39] within PB and GEOBIA classification approaches. The secondary goal is to use different accuracy assessment metrics to determine which of the two tested classification algorithms (SVM and MLC) most reliably distinguishes olive tree crowns, and which approach is more accurate (PB or GEOBIA). The third goal is to add false polygon samples for COR, COM and OQ metrics, and use them to calculate Total Accuracy (TA).
The paper includes the following sections. 1 3.5. Results of Classification Algorithms and 3.6. Accuracy of Classification Algorithms. The last section is 4. Conclusions.

Study Area
The study area for the extraction of olive tree crowns is the topographic basin of the settlement of Sali ( Figure 1). It largely includes the Saljsko polje olive grove (202.1 ha), which protects the Natura 2000 ecological network as a special botanical reserve. The area is located in the south-eastern part of Dugi Otok, and contains olive trees up to 700 years old which, along with the size of the trees, makes this a unique ecological area within this area [40].

The Methodological Framework of the Research
The methodology is divided into the following steps: field research (1); development of digital orthophoto (DOP) and UAV MS (2); UAV MS segmentation (3); adding test samples (4); PB classification using two classification algorithms (MLC and SVM) (5.1); GEOBIA classification using two classification algorithms (MLC and SVM) (5.2); estimation of the accuracy of the model and selection of the best classification algorithm (6) (Figure 2). The methodology is divided into the following steps: field research (1); development of digital orthophoto (DOP) and UAVMS (2); UAVMS segmentation (3); adding test samples (4); PB classification using two classification algorithms (MLC and SVM) (5.1); GEOBIA classification using two classification algorithms (MLC and SVM) (5.2); estimation of the accuracy of the model and selection of the best classification algorithm (6) (Figure 2).

Field Research
The field research (Figure 2(1)) was conducted on June 27 and 28, 2020. This period is most suitable for aerial photogrammetric imaging of olive trees due to the fact that pruning is already done, the culmination of vegetation and the minimal presence of shadows [41]. Two aerial photogrammetric surveys of the topographic basin of the Sali settlement were performed. The first survey used a camera that records the visible part of the spectrum (RGB), and the second used a multispectral camera. In the process of collecting RGB images, a UAV DJI Matrice 210 RTK V2 was used, on which a Zenmuse X7 camera was mounted (Figure 2 (1)), while a DJI Matrice 600 Pro on which a MicaSense RedEdge-MX (Figure 2(1)) multispectral camera was mounted was used to collect multispectral images. An RTK-GPS Stonex S10 (Figure 2(1)) was used to collect ground control points (GCPs) and checkpoints (CPs).

Field Research
The field research (Figure 2(1)) was conducted on June 27 and 28, 2020. This period is most suitable for aerial photogrammetric imaging of olive trees due to the fact that pruning is already done, the culmination of vegetation and the minimal presence of shadows [41]. Two aerial photogrammetric surveys of the topographic basin of the Sali settlement were performed. The first survey used a camera that records the visible part of the spectrum (RGB), and the second used a multispectral camera. In the process of collecting RGB images, a UAV DJI Matrice 210 RTK V2 was used, on which a Zenmuse X7 camera was mounted (Figure 2(1)), while a DJI Matrice 600 Pro on which a MicaSense RedEdge-MX (Figure 2(1)) multispectral camera was mounted was used to collect multispectral images. An RTK-GPS Stonex S10 (Figure 2(1)) was used to collect ground control points (GCPs) and checkpoints (CPs).

Data Acquisition
The first step was to mark and collect points on a local geodetic basis. GCPs were collected to achieve a better absolute orientation of the model. A total of ten GCPs and five CPs were marked and measured in the official projection coordinate reference system of the transverse Mercator projection (HTRS96/TM). The points were collected at different altitudes, considering the rules for the spatial arrangement of placement of landmarks in photogrammetry (distribution throughout the entire survey plan) [42] (Figure 1). The next step was to create an optimal flight plan. This included selecting mission types considering terrain morphology, the research object, and the distribution of GCPs. The development of missions via UAV was performed in the DJI GS Pro software. Seven double grid missions with front and side overlap of 80% were planned for the RGB camera's data acquisition in the topographic basin settlement of Sali, while seven single grid missions with front and side overlap of 80% were planned for data acquisition by the multispectral camera. Considering the comprehensiveness of the terrain and the desired spatial resolution (ground sampling distance: GSD) for DOP (GSD = 5 cm), the average flight or acquisition altitude was about 200 m, and for UAV MS (GSD = 18 cm), it was 260 m. The compasses and inertial measurement systems (IMUs) of both UAVs were then calibrated, and a multispectral camera radiometric calibration was performed using a reflection calibration panel (CRP2). The multispectral camera was calibrated before and after each mission, in order to accurately display the light conditions during flight. The calibration process was performed in such a way that the CP2 was lowered onto a flat surface. Then, the camera was connected to the configuration page via a Wi-Fi network. Special attention was given to ensuring that the panel was not in the shade when taking a calibration photo, and that the sensor was at least 1 m above the CP2. Finally, the aerial photogrammetric recording was performed.

UAV Imagery Processing
The second step in extracting olive tree crowns was the production of DOP and UAV MS images ( Figure 2(2)). The collected RGB and multispectral images were processed using Agisoft Metashape Professional 1.5.1., one of the most commonly used software packages for photogrammetric image processing [43,44]. Thanks to implemented structurefrom-motion (SfM) and multi-view algorithms, Agisoft enables 3D modelling based on reconstructing 3D structures from overlapping 2D images [45][46][47]. First, connection points were generated, and models were oriented with the help of collected GCPs. Then, dense point clouds and polygon networks were created, from which digital surface models (DSM) ( Figure 3C), DOP ( Figure 3A) and UAV MS ( Figure 3B) were derived.

Segmentation
The third step was the segmentation of UAV MS (Figure 2(3)) based on the Mean shift approach [19] within ArcGIS. The Segment Mean Shift tool within ArcGIS identifies features or segments in imagery by grouping neighboring pixels together that have similar spectral, spatial, and geometric characteristics [19]. Since the characteristics of the image segments depend on the spectral detail, spatial detail and the minimum segment size, the optimization of the values of stated parameters was achieved. An iterative process (n = 64) was performed. The best combination of parameter values was selected based on the visual interpretation of the UAV MS segmentation results.

Adding Test Samples
The fourth step was to add test samples (Figure 2(4), Supplementary Material Figure S1). This refers to the process of collecting test samples and verifying them. Samples were collected for the following 18 classes: Young Olive Trees, Old Olive Trees, Older Olive Trees, Oldest Olive Trees, Tile, Sheet Metal, Sheet Metal 2, Asphalt, Concrete, Shadow, High Vegetation, Macadam, Bare Stone, Quarry, Sea, Grass, Shrubby Vegetation, Brown Soil. Due to the possible input distortions of the sharpened multispectral image, a larger number of samples were marked. The collected test samples were checked by the cross-validation method. In total ≈ 1200 samples for all classes were collected.
Remote Sens. 2022, 14, x FOR PEER REVIEW 8 of 17 generated ( Figure 3C). Based on the generated DSM and point clouds, a 5 cm spatial resolution DOP was created ( Figure 3A), containing three visible channels (RGB).

Derivation of UAVMS
A total of 7245 high-quality images were collected in the planned seven missions. Five images were collected at each recording location, one for each multispectral camera channel (red, green, blue, red edge, near-infrared). It follows that a total of 7245 × 5 = 36,280 images were taken. By processing the images within Agisoft Metashape 1.5.1., very-highresolution UAVMS were generated. The combination of channels determined that the differences between vegetation species are best observed in the layout of channels 5-4-1 (Figure 3B). The UAVMS, with this channel order, served as the basis for extracting classes with the PB approach, while for the GEOBIA approach, segmentation of the same image was performed.

Segmentation
As in [29], the iterative process of testing segmentation parameters based on trialerror procedure yielded optimal values for olive tree crowns extraction by the GEOBIA approach. Of the 64 derived models, a model containing the following parameter values was selected: (a) spectral detail = 18/20; (b) spatial detail = 15/20; (c) minimum segment size = 10/20 ( Figure 3D). Given that the spectral detail is conditioned by the spectral resolution of UAVMS [58], which affects the differentiation of different vegetation species, the

Classification
The fifth step was classification ( Figure 2 Figure S2). This was created in Model Builder within ArcGIS. PvO-ACP allows simultaneous generation of PB and GEOBIA models via access based on selected parameters. The generated models were then reclassified into 12 classes ( Figure 4) and finally into two classes: Olive Tree and Other, as in [28] (Figure 7).

Accuracy of Classification Algorithms
Using COR, COM and OQ metrics on ROTs, the results obtained showed that the GEOBIA-SVM model had the highest COR value (0.9064), which is similar to the results in [34] (

Accuracy Assessment
The sixth step was to assess the accuracy of the models and select the best classification algorithm (Figure 2(6)). Estimating the accuracy of the generated models was based on COR, COM and OQ metric indicators. These metrics quantify the relationship between reference objects (reference olive trees, R OT ) and derived objects (classified olive trees, C OT ) and examine the accuracy of the executed classification [48]. The accuracy assessment was performed based on 13 polygon features of R OT s (R OT1 -R OT13 ) ( Figure 4A2,B2,C2,D2 and Figure 7). R OT s were selected using the Create Accuracy Assessment Points tool within ArcGIS. Olive trees on which a pixel was located, or was closest to them, were vectorized at a scale of 1:25 from the produced DOP with a spatial resolution of 5 cm. The authors in [26] Remote Sens. 2022, 14, 757 8 of 18 also manually delineated tree crowns in their research, but they vectorized all the trees. The overlap area (A O ) of all four C OT s was calculated. The values were used to assess the accuracy of the classification algorithms according to the following formulas: where A o is the overlap area of R OT and C OT and A C OT is the total area of C OT where A o is the overlap area of R OT and C OT and A R OT is the total area of R OT where A o is the overlap area of R OT and C OT , A R OT is the total area of R OT and A C OT is the total area of C OT . The COR, COM and OQ metrics values vary in the range of 0-1. Higher values indicate a higher match between reference and classified objects, i.e., higher accuracy of the classification algorithm [49].
Since it can be seen from the generated models that the classification algorithms in the PB models overestimated the area of olive tree crowns, it was necessary to add another 13 polygon features, representing false olive trees (F OT ). This is the first time, to the best of our knowledge, that false polygon samples have been added to these metrics. F OT s were added on the same principle as R OT s. Then, COR, COM and OQ values for F OT s were calculated. The same methodology was applied as for R OT s, except that in this case a lower value represents higher accuracy. The Total Accuracy (TA) was calculated by subtracting the F OT indicator value from the R OT indicator value according to the formulas below: TA values can vary between −1 and 1. A higher value represents higher accuracy. The second accuracy assessment was performed using Producer Accuracy (PA) and User Accuracy (UA) for both classes. PA represents the probability that a reference pixel was classified correctly while UA represents the probability that a classified pixel represents that class on the ground [50,51]. Overall Accuracy (OA) was also calculated for estimation of classification rate correctness. It represents the quotient of the total number of correct pixels and the total number of pixels in the error matrix [50,51]. The accuracy assessment using these indicators was calculated in two ways. The first way was to randomly add 500 points using the Create Accuracy Assessment tool. Each generated point received ground truth data and classification data (for each of the four generated models). The values of PA, UA and OA were then calculated using the Create Confusion Matrix tool within ArcGIS, based on the formulas below: where p ii is the major diagonal element for class I, p +i is the total number of observations in column i (bottom margin), p i+ is the total number of observations in row i (right margin) and m is the number of rows, columns in the error matrix. PA, UA and OA values can vary between 0 and 1. A higher value represents higher accuracy. Another way to calculate PA, UA and OA values was to create a point feature for each pixel based on the generated R OT and F OT polygons. Considering the spatial resolution of the UAV MS , as described in [34], the Create Fishnet tool within ArcGIS generated a grid of polygons of 18 * 18 cm within the R OT s and F OT s, with centroids that were used as input data to assess the accuracy of the models. Then, in the same way, the pixel value for each generated model was added for all point features, and PA, UA and OA were calculated using the Create Confusion Matrix tool.
Tertiary accuracy assessment was performed using the receiver operating characteristics curve (ROC) and the calculation of the area under the curve (AUC), which are widely used methods for estimating the accuracy of classification algorithms [52][53][54][55]. AUC values vary in the range of 0-1, with higher values representing greater accuracy of the model. Values <0.6 represent poor accuracy; 0.6-0.7 average accuracy; 0.7-0.8 good accuracy; 0.8-0.9 very good accuracy; and > 0.9 excellent accuracy [56,57]. The creation of ROC curves was automated with the Calculate ROC Curves and AUC Values tools within Ar-cGIS. The same 500 random points as those created within R OT s and F OT s for calculating PA, UA and OA were used as input data to assess the accuracy of the models and select the best classification algorithm.

Derivation of DSM and DOP
A total of 6587 high-quality images were collected in the planned seven double grid missions. The total error in five CPs used for the accuracy assessment of the generated model CPs was 5.83 cm. Suppose the spatial resolution of the aerial photogrammetric images and the purpose of the model are considered. In that case, this accuracy of the developed models satisfies the needs of the analyses for which the created models were used. By interpolating 1.5 * 10 9 dots within a dense cloud, a 5 cm spatial resolution DSM was generated ( Figure 3C). Based on the generated DSM and point clouds, a 5 cm spatial resolution DOP was created ( Figure 3A), containing three visible channels (RGB).

Derivation of UAV MS
A total of 7245 high-quality images were collected in the planned seven missions. Five images were collected at each recording location, one for each multispectral camera channel (red, green, blue, red edge, near-infrared). It follows that a total of 7245 × 5 = 36,280 images were taken. By processing the images within Agisoft Metashape 1.5.1., very-high-resolution UAV MS were generated. The combination of channels determined that the differences between vegetation species are best observed in the layout of channels 5-4-1 ( Figure 3B). The UAV MS , with this channel order, served as the basis for extracting classes with the PB approach, while for the GEOBIA approach, segmentation of the same image was performed.

Segmentation
As in [29], the iterative process of testing segmentation parameters based on trial-error procedure yielded optimal values for olive tree crowns extraction by the GEOBIA approach. Of the 64 derived models, a model containing the following parameter values was selected: (a) spectral detail = 18/20; (b) spatial detail = 15/20; (c) minimum segment size = 10/20 ( Figure 3D). Given that the spectral detail is conditioned by the spectral resolution of UAV MS [58], which affects the differentiation of different vegetation species, the selected value of the spectral detail allowed the separation of olive trees from other vegetation. The value of the spatial detail parameter, which determines the importance of feature proximity in the multispectral model, enabled the isolation of individual trees and reduced the influence of generalization during classification. The minimum segment size was set at 10 pixels which, given the spatial resolution of the UAV MS of 18 cm, allowed mapping of all olive tree crowns larger than 0.324 m 2 .

Adding Test Samples
Numerous factors such as shadows caused by tall objects or terrain morphology, sunlight angle, vegetation physiology, etc., affect differences in the spectral characteristics of elements of the same class within the same image [59]. For this reason, a total of ≈1200 samples were collected within 18 classes. Four classes related to olive trees whose spectral differences are influenced by ecological conditions, tree age, agronomic processes, variety, leaf growth rate and other abiotic and biotic stress factors [60].

Results of Classification Algorithms
SVM and MLC models for the PB and GEOBIA approaches ( Figure 4A-D) were generated by the developed PvO-ACP tool. The PB approach has been observed to overestimate olive tree crown areas, especially in areas where pine forests predominate ( Figure 4A1,B1). This is because the pixel as the minimum mapping unit is responsible for the "salt and pepper" effect. The GEOBIA-SVM model recognized the fewest olive trees in the pine forest ( Figure 4C1), while the GEOBIA-MLC model had slightly lower results ( Figure 4D1). The algorithms recognized olive trees in the shadows within all LCMs, but mostly in those generated by the PB approach, particularly the MLC classification algorithm. PB models are "grainy" (Figure 4A2,B2), while GEOBIA models are more compact ( Figure 4C2,D2), especially the SVM model ( Figure 4C2).

Accuracy of Classification Algorithms
Using COR, COM and OQ metrics on R OT s, the results obtained showed that the GEOBIA-SVM model had the highest COR value (0.9064), which is similar to the results in [34] ( Figure 5C). The PB-SVM model had the highest value of the COM (0.8772), as well as of the OQ (0.7767) ( Figure 5A), which proves it to be the most accurate model. However, using F OT s, it has been proven that the GEOBIA-SVM model overestimates the surface area of the Olive Tree class by the least amount. GEOBIA-SVM had the best index values (COR = 0.3796; COM = 0.0159; OQ = 0.0157) (Table 1C). By calculating the TA, the GEOBIA-SVM model was shown to be the most reliable (TA COR = 0.5269; TA COM = 0.8110; TA OQ = 0.7450) ( Table 2). In addition, it has been proven that false samples in these metrics affect the change in accuracy results.
The confusion matrix results showed that the GEOBIA-SVM model had by far the largest UA and OA values, regardless of the number and arrangement of points at which accuracy was tested (Table 3C, C1). The OA values of 0.926 and 0.980 are similar to the results in [27], where an OA value of 0.93 was obtained after certain manual corrections to the final model. Both MLC models had slightly lower accuracy but were still better than results obtained in [32] where an OA value of 0.69 was obtained for an MLC model generated by supervised classification. It has been proven that an increase in the number of test points increases the model's accuracy according to the OA indicator, but the value of UA decreases significantly for the Olive Tree class. Furthermore, with an increase in the number of test points, the value of PA increased, except in the case of PB-SVM, where it decreased slightly but was still the highest (Table 3A, A1). As in [30], the OA proved that GEOBIA more reliably separates tree species than the PB approach. From the results, it can be seen that there are some general features in the results of confusion matrices derived based on a larger number of test samples. These refer to relatively low values of the UA metric, ranging from 0.093 (Table 3B1) to 0.497 (Table 3C1). This may indicate the problem of overestimating the number of olive trees in a specific area. Therefore, this approach must be applied with caution, and additional ground validation data used in addition to MS.  Table 2). In addition, it has been proven that false samples in these metrics affect the change in accuracy results.    Figure 7) is the most reliable classification algorithm for extracting olive tree crowns from UAV MS imagery ( Figure 6). Its accuracy has excellent values of 0.904 when using 500 random points ( Figure 6A) and 0.929 when using points within R OT s and F OT s ( Figure 6B). The GEOBIA-MLC model also has excellent accuracy (0.901) in points within R OT s and F OT s and very good accuracy (0.864) within 500 random points. SVM (PB) has very good accuracy within both approaches (0.833 and 0.874), while the less accurate model is PB-MLC, also with very good accuracy, but much lower at 0.826 and 0.813. The AUC indicator derived for 13 R OT s and 13 F OT s indicates the problem of shadows, which classification algorithms, especially in PB analyses, recognize as an olive tree, mostly in areas of dense pine forest and tall buildings. The SVM classification algorithm has proven to be an excellent solution for reducing shadow problems in GEOBIA analyses, while in the case of PB analyses, SVM is also a better solution than MLC as the latter produces "noise" (Figure 7).  rived for 13 ROTs and 13 FOTs indicates the problem of shadows, which classification algorithms, especially in PB analyses, recognize as an olive tree, mostly in areas of dense pine forest and tall buildings. The SVM classification algorithm has proven to be an excellent solution for reducing shadow problems in GEOBIA analyses, while in the case of PB analyses, SVM is also a better solution than MLC as the latter produces "noise".

Conclusions
SVM and MLC LCMs of the topographic area of the Sali settlement on the island of Dugi Otok were generated using the PB and GEOBIA approaches. All generated LCMs were reclassified into two classes: Olive Tree and Other. Accuracy assessment metrics showed that the GEOBIA approach generated more accurate models than the PB approach. Therefore, it was found that the applied classification algorithms achieved better results on the segmented image. Since GEOBIA groups pixels into homogeneous meaningful "superpixels", various types of "noise" within the shaded areas (forests and tall buildings) occur much less frequently. These problems can often occur in the PB approach because classification algorithms within the PB approach classify each pixel separately. However, PB classification algorithms can produce results with satisfactory accuracy.
Although the accuracy assessment indicated that SVM (GEOBIA) was the best algorithm for extracting olive tree canopies, it is necessary to point out certain limitations. As the results indicate (Table 3), there may be a problem of overestimation (low value of UA)

Conclusions
SVM and MLC LCMs of the topographic area of the Sali settlement on the island of Dugi Otok were generated using the PB and GEOBIA approaches. All generated LCMs were reclassified into two classes: Olive Tree and Other. Accuracy assessment metrics showed that the GEOBIA approach generated more accurate models than the PB approach. Therefore, it was found that the applied classification algorithms achieved better results on the segmented image. Since GEOBIA groups pixels into homogeneous meaningful "superpixels", various types of "noise" within the shaded areas (forests and tall buildings) occur much less frequently. These problems can often occur in the PB approach because classification algorithms within the PB approach classify each pixel separately. However, PB classification algorithms can produce results with satisfactory accuracy.
Although the accuracy assessment indicated that SVM (GEOBIA) was the best algorithm for extracting olive tree canopies, it is necessary to point out certain limitations. As the results indicate (Table 3), there may be a problem of overestimation (low value of UA) of the number of olive trees in a specific area. Therefore, this approach must be applied with caution, and ground validation data should be used.
The COR, COM and OQ metrics proved that all classification algorithms overestimated the area of olive trees (high false-positive rate), except the SVM (GEOBIA), which does so rarely, as 7 out of 13 false samples did not recognize the Olive Tree class. For this reason, the TA results indicated that the GEOBIA-SVM model was the most accurate (TA COR of 0.527, TA COM of 0.811, TA OQ of 0.745). Furthermore, other metrics used also showed that GEOBIA-SVM was the most accurate classification method. More precisely, GEOBIA-SVM achieved an OA of 0.926 or 0.980 and an AUC value of 0.904 or 0.929, depending on the number and arrangement of accuracy assessment points. Limitations on the software used and the lack of licenses for other software, such as eCognition, limited the choice of algorithms. In future research, the accuracy of several segmentation methods (Multiresolution, Spectral Difference, etc.) and other classification algorithms (Hierarchical Classification, Random Forest, Fully Convolutional Networks, Deep Convolutional Neural Networks, etc.) will be examined. Furthermore, the influence of spectral resolution and UAV flight settings (flight altitude and camera calibration) on the accuracy of GEOBIA and PB classification algorithms will be determined.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.