2.1

Study Area and Dataset

This study was conducted in parts of Dieng Plateau, Central Java, Indonesia. The extent of the study area is between 377798 to 378410 meters East and between 9201400 to 9202200 meters North (based on UTM coordinate system zone 49M). The study area covers 42 ha which consist of agricultural land, forest land, and scrub area. The selection of this area because of the various vegetation types which making it possible to applied various object features on the GEOBIA process. The study area is shown in Figure 1.

Figure 1.

The map of study area

A WorldView-2 image acquired on August 6th, 2018 was used in this study. The image obtained from DigitalGlobe (www.digitalglobe.com) and available at “Standard2A” correction level which means had been corrected on geometric with brightness value digital number. Therefore, standard radiometric correction to surface reflectance value was needed. Image fusion using Gram-Schmidt pan-sharpening method also performed in this study to maximize the visual image quality from 1.86 meter to 0.5 meters pixel size. Image fusion only applied to 450-800 nm (Panchromatic) wavelength on multispectral bands, consist of blue (450-510 nm), green (510-580 nm), yellow (585-625 nm), red (630-690 nm), red-edge (705-745 nm) and near-infrared 1 (770-895 nm).

2.2

Reference Data of Vegetation Types Polygons

Reference data is needed to assess the accuracy of segmentation result. In this case, the reference data was obtained from visual interpretation of the WorldView-2 pan-sharpened image. Vegetation objects was delineated by on-screen digitization technique with considered the minimum mappable unit (MMU) rules, where the smallest object could be recognized from image is 2 x 2 mm [10]. The targeted map output was 1:5,000, therefore the MMU in this study was 10 x 10 m, the one of example of 10 x 10 m objects in the field is part of agricultural field. The visual interpretation process was carried out up to 1:1,250 zooming scale level.

2.3

Multiresolution Segmentation

Multiresolution Segmentation was applied by combination of three parameters of the multiresolution segmentation algorithm that implemented in eCognition Developer 9.01 (www.ecognition.com). This algorithm was developed by Baatz and Schäpe [11] with the aim of reducing heterogeneity of images by creating object segments with accurate results even though carried out at various scales and data types. The selection of this algorithm due to the consideration of this study purpose which focused to find out the relevant parameters in determine the optimum parameters for delineating vegetation types. The segmentation algorithm is based on a region growing technique which places seed pixels over an entire image and groups surrounding pixels to the local seeds, if they meet specific criteria [11]. The aim of this algorithm is to reduce heterogeneity of image by creating objects segments with accurate results even though carried out at various scales and data types.

Multiresolution segmentation has two main components: (1) decision heuristics to regulate the image objects that will merge at each step and (2) definition of a homogeneity of image objects to calculate the degree of fitting for a pair of image objects [11]. This algorithm produces object segments based on scale parameter, color, shape, smoothness and compactness. Scale parameter is an abstract term that determines the maximum allowed heterogeneity for the resulting image objects. By modifying the value, the size of image objects can be varied. The value of the Shape field modifies the relationship between shape and color criteria. By modifying the shape, it means define the color criteria (color = 1 shape). In effect, by decreasing the value assigned to the Shape field, means define to which percentage the spectral values of the image layers will contribute to the entire homogeneity criterion. The compactness is used to optimize image objects regarding the compactness. These criteria should be used when different image objects which are rather compact but are separated from non-compact objects only by a relatively weak spectral contrast. By modifying the compactness, it would define the smoothness criteria [6].

2.4

Segmentation Quality Metrics

The quality of segmentation results is important to know. The low accuracy of a segmentation results affects the accuracy of the classification results [12]. This study would assess the accuracy from the most optimal segmentation that have performed. The optimal segmentation result would be overlaid with reference map which have produced before. The following table (Table 1) will describe the metrics and formula for accuracy assessment.

Table 1.

Description of segmentation quality metrics

3.1

Reference Data of Vegetation Types

A reference data needed for assess the quality of segmentation. In this study, the reference data produced from visual interpretation of vegetation types using pan-sharpened WorldView-2 (0.5-pixel size). Delineation of different types of vegetation was carried out by on-screen digitization based on some key interpretations, such as colors, texture, shapes, sizes, patterns, shadows and associations. The types of vegetation that found in the study area consist of agricultural land, bare land, forest, shrub and tree. Shadows were included in classification because in some case, they covered vegetation objects and would be affected the result of segmentation. The reference data from visual interpretation are shown in Figure 2. This data would be over-laid with the segmentation result to know the accuracy of segmentation.

Figure 2.

Reference data of vegetation types resulted from image visual interpretation.

3.2

Multiresolution Segmentation

Segmentation is the first step in GEOBIA which determine the object boundaries. This study focuses on recognizing the types of vegetation by applied multiresolution segmentation to WorldView-2 pan-sharpened image. Multiresolution segmentation was performed using scale parameter and composition of homogeneity criterion, consists of shape and compactness. The segments will be more detailed as the scale parameter decreases. Simulations by combining of various parameters was carried out to get the optimal segmentation (i.e. able to properly separate different types of vegetation). Based on the image, the types of vegetation can be distinguished from the textures and shapes, therefore the first simulation applied the same weight of shape and compactness criterion. The results of segmentation created from different scale parameters can be seen in Figure 3.

Figure 3.

Segmentation created from different scale parameters (with shape:compactness ratio of 0.5:0.5); (a) SP 10, (b) SP 30, and (c) SP 50.

The results of segmentation in Figure 3 show poor segmentation with rounded segments. Segmentation using the shape and compactness criterion coincidely to the image were not appropriate. The object boundaries were too rough and not able to separated objects properly.

The other simulations used only shape criterion with various weight (with compactness = 0). The higher weight given to the shape criterion, the more detailed segments would be produced. This simulation produced better separated segments for the different objects, particularly on regular shapes object as agricultural land (Figure 4). However, this segmentations were not able to properly separate trees and irregular vegetation objects.

Figure 4.

Segmentation created from different weights of compactness (with shape = 0 and scale parameter = 20); (a) 0.2, (b) 0.5, and (c) 0.8.

Simulations were also applied using only compactness criterion (with shape = 0). The results show the best segmentation that could separated the different types of vegetation according to the actual border. The weight that given to the compactness criterion was 0.5 and there was no significant segments change when applied different weight. Therefore trials and errors to obtain the most optimal segments were carried out by adjust the scale parameters. Figure 5 shows the segments that increasingly detailed as the weight of scale parameter decreases.

Figure 5.

Segmentation created from different SP (with shape:compactness = 0:0.5); (a) 20, (b) 10, and (c) 5.

Based on Figure 5, by applied the compactness weight = 0.5, means the smoothness criterion weight = 0.5. In this case, the position of these criterions were balanced. Both compactness and smoothness were important in defining vegetation objects in the study area. Between three segmentations in Figure 5, the result of scale parameter of 5 (with shape:compactness = 0:0.5) are the most optimal segmentation. The segments could separated different types of vegetation, consist of agricultural land, bare land, forest, shrub and tree (Figure 6).

Figure 6.

The segmentation of types vegetation: (a) agricultural land, (b) bare land, (c) forest, (d) shrub and (e) tree.

3.3

Segmentation Quality Metrics

The optimal segmentation with the scale parameter of 5 (with shape:compactness = 0:0.5) would be assesst in order to know the quality of segments. The detailed of accuracy can be seen in Table 2.

Table 2.

The quality of optimal segmentation result

Based on the calculation of segmentation quality, this study produced 0.67 of Quality rate (Qr) on segmentation result or around 67%. The low accuracy in this case due to the considerable over-segmentation which also causes the high AFI value. However, the value of under-segmentation is very low which means almost all of vegetation types on reference data could be separated properly.