Accuracy assessment measures for image

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Accuracy assessment measures for image segmentation goodness of the Land Parcel Identification System (LPIS) in Denmark a

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Alessandro Montaghi , René Larsen & Mogens H. Greve

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Department of Agroecology , Faculty of Science and Technology, Aarhus University, Blichers Allé 20 , DK-8830 , Tjele , Denmark Published online: 24 Jul 2013.

To cite this article: Alessandro Montaghi , Ren Larsen & Mogens H. Greve (2013) Accuracy assessment measures for image segmentation goodness of the Land Parcel Identification System (LPIS) in Denmark, Remote Sensing Letters, 4:10, 946-955 To link to this article: http://dx.doi.org/10.1080/2150704X.2013.817709

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Remote Sensing Letters, 2013 Vol. 4, No. 10, 946–955, http://dx.doi.org/10.1080/2150704X.2013.817709

Accuracy assessment measures for image segmentation goodness of the Land Parcel Identification System (LPIS) in Denmark

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ALESSANDRO MONTAGHI∗ , RENÉ LARSEN and MOGENS H. GREVE Department of Agroecology, Faculty of Science and Technology, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark (Received 16 May 2013; in final form 14 June 2013) This letter evaluates the performance of eCognition’s multi-resolution segmentation algorithm on the task of delimiting agricultural parcels in Denmark using very high spatial resolution 8-band WorldView-2 (WV2) imagery. Fifty-seven different scale parameters setting, ranging from 100 to 1500, was employed in order to assess the quality of segmentation. An accuracy assessment was performed using seven metrics based on the topological or geometric similarity between segmented polygons and reference polygons, which were derived through manual delineation. The results indicate that (1) segmentation accuracy is influenced by the size of the reference polygons and (2) the presence of clear boundaries (e.g. hedgerow, ponds, ditches and road) drives the segmentation algorithm when the scale parameter exceeds a certain value.

1.

Introduction

In 1992, within the Common Agricultural Policy (CAP) regulatory framework, the European Union (EU) decided to establish an Integrated Administration and Control System (IACS) in order to control the financial aids to farmers. In order to obtain the assistance contributions, the farmers need to declare their exact parcels area. The tasks of the IACS are to administer and control if the farmers’ declarations are correct. The CAP was fundamentally reformed in the year 2003 (Schmid and Sinabell 2007), with the decision to establish the Land Parcel Identification System (LPIS) from a nongraphical version to a digital and geo-referenced format by 1 December 2005 (Sagris and Devos 2008, Inan et al. 2010). The reform was necessary because, compared to the real situation, a high percentage of declared areas were incorrect (Oesterle and Wildmann 2003). In order to minimize the irregularities, from the end of 2005, the EU member countries have been using remote-sensing data integrated with Geographic Information Systems (GIS) for the spatial reference identification of the agricultural parcels. Although the implementation of the control system is subject to the single EU state, in several countries LPIS is implemented in a GIS environment using digitized boundaries hand drawn from orthophotos (Oesterle and Hahn 2004). The area inside boundaries (i.e. farmer field parcel) is defined as homogeneous agriculture land minus the area of the not eligible elements (e.g. building, hedgerow, ponds, ditches and groups *Corresponding author. Email: [email protected]

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of trees). This information is then updated regularly to monitor the evolution of the land cover and the management of the crops. The field parcel identification and updates are time-consuming processes mainly made by human phointerpreters. Furthermore, the quality of the results of a manual delineation is subjective and variable between analysts and may lead to different results (Edwards and Lowell 1996, Næsset 1999, Zhu et al. 2002). A single interpreter may even provide different results on two occasions (Nantel 1993). Additionally, differences in the parcel area estimation are often caused by positional errors of the individual boundaries forming the perimeter of the land parcel (Næsset 1998). Finally, the accuracy to draw an identified boundary is affected by the interpreters’ ability and experience to resolve difficult tasks (Gardin et al. 2011). A possible solution to these problems is provided by segmentation techniques for remotely sensed images. Segmentation is a process of identification of the edges of a finite set of non-overlapping homogeneous objects that subdivide the image into tessellated regions (Campbell and Wynne 2011). During the segmentation, adjacent objects are merged or divided following specified criteria of similarity/ dissimilarity. The approach of segmentation is similar to human visual interpretation of digital image (Lillesand et al. 2007), and the analyst has the ability to set the criteria that control the measures used to assess similarity and dissimilarity. The segmentation of images has a long history for remote-sensing applications, and the problems related with different algorithms and image resolutions have been addressed by several authors (Cannon et al. 1986, Zamperoni 1992, Hay et al. 2003, Muñoz et al. 2003, Wang et al. 2010). However, it’s difficult to achieve an ‘optimal’ segmentation result because there are a wide range of parameters to be set depending on the type of application, the environmental conditions and the kinds of remotely sensed images; moreover, different segmentation software use different parameter combinations (Myint et al. 2011). Several remote-sensing software have embedded segmentation and extraction features algorithms in order to be able to automate tasks that were once only possible using time-consuming on-screen digitizing (Neubert and Meinel 2003, Castilla et al. 2008, Wang et al. 2010). In particular, the multi-resolution segmentation algorithm (Baatz and Schäpe 2000) embedded in the commercial software, Definiens Developer 8.64 (formerly eCognition), of Trimble (Sunnyvale, CA, USA) is widely used due to its ability to fully explore the information content of very high spatial resolution imagery. The objective of this letter was to evaluate the segmentation quality for the field parcels delimitation using very high spatial resolution 8-band WorldView-2 satellite (WV2) data and the multi-resolution segmentation algorithm embedded in eCognition.

2. Materials and methods 2.1 Study area The study site was located in north-western Zealand, in the eastern part of Denmark. The area was about 278 km2 (upper left longitude 11◦ 29 30 and latitude 55◦ 44 40 ; lower right longitude 11◦ 41 40 and latitude 55◦ 33 20 ), with elevation ranging from –0.3 m to 106 m above sea level. The area selected covered rural segments (crops, woodlots and lakes/ponds) and urban regions (farms, roads and small towns), giving a diversity of rural land-use and land-cover classes.

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2.2 Image data and pre-processing Launched in October 2009, the WV2 is the first commercial satellite with eight high spatial resolution (2 × 2 m) multispectral (MS) bands in addition to a panchromatic (Pan) band with spatial resolution of 0.5 × 0.5 m (DigitalGlobe 2010). The WV2 image was acquired under cloudless condition over the study area on 18 April 2011. The radiometric resolution of the data set was 16 bit. The WV2 was atmospherically corrected using the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) module of the commercial software ENVI version 4.8 (ITT Visual Information Solution, Boulder, CO, USA; ITT 2010). In order to take advantage of the synergistic effectiveness of the high resolution Pan band with the MS bands, eight pan-sharpened WV 0.5-m spatial resolution images (WV2 PS) were created by fusing the Pan WV2 with each MS WV2 imagery. The Gram–Schmidt Spectral Sharpening algorithm, invented by Laben and Brower (2000) and patented by Eastman Kodak (Laben and Brower 2000), Aiazzi et al. 2006, was used in this study. 2.3 Analysis of segmentation quality The multi-resolution segmentation available in eCognition is a heuristic algorithm based on the Fractal Net Evolution Approach (FNEA) (Baatz and Schäpe 2000). FNEA is a bottom-up merging technique that starts with one-pixel objects and a pairwise comparison of its neighbours in order to merge smaller image objects into larger ones (Baatz et al. 2004). The merging criterion minimizes the resulting summed heterogeneity of the objects weighted by their size in pixel (Benz et al. 2004). The operator controls the segmentation outcome by setting several user-defined parameters including, (1) scale, (2) colour/shape ratio, (3) smoothness and (4) compactness. The scale parameter determines the maximum allowed heterogeneity within an object, and consequently the size of segmented objects can be varied by varying the scale parameter value. The smaller number of scale generates objects with small size, whereas the higher number of scale will generate objects with large size. As discussed in Myint et al. (2011), the scale parameter can be considered the most crucial parameter of image segmentation. In order to obtain an exhaustive description of the science behind all four criteria, the reader is referred to Baatz et al. (2004) and Navulur (2006). Using the input of the eight WV2 PS band images, the compactness criterion was set to 0.8, whereas the smoothness received the remaining weight of 0.2. After testing and evaluating qualitatively different colour/shape setting, colour was set to 0.4 (consequently shape was set to 0.6) in order to balance the attention between the spectral components of the pixels and the geometric characteristics of the objects. Segmentations were performed at 57 different scale parameters ranging from 100 to 1500. A number of studies have used metrics to assess segmentation accuracy by measuring both topological and geometric similarity between segmented objects and reference objects (e.g. Yang et al. 1995, Lucieer and Stein 2002, Zhan et al. 2005, Möller et al. 2007, Weidner 2008, Clinton et al. 2010). In this study, we used the farmer field parcels of Denmark as reference polygons, which were derived from the manual delineation of 2011 aerial digital photographs. From the National database, the polygons selected were those inside the WV2 image coverage. In order to avoid possible edge effects (Zhou and Lam 2008), only polygons further than 500 m from the image border were considered in the computation of accuracy. Initially, a total of 3335 polygons were selected for the study, with an average size of 4.31 ha (standard deviation of

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6.01 ha) and a range from 0.03 to 73.63 ha. The polygons were divided into four areabased classes: 0–5 ha (class 1), >5–10 ha (class 2), >10–15 ha (class 3) and >15 ha (class 4). A thresholding method was applied before calculating the metrics in order to reduce the false effects caused by small overlapped regions between the reference and segmented object. In accordance with Pu and Landry (2012), the segmented object was considered in the analysis if the centroid was within the reference object. The metrics selected included (1) the relative area of an overlapped region to a reference object (RAor ), (2) the relative area of an overlapped region to a segmented object (RAos ), (3) the quality rate (qr), (4) the SimSize, and (5) the Area Fit Index (AFI): 1  Ao (i) × 100% n Ar

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1  Ao (i) × 100% n As (i)

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1 Ao (i) 1− n Au (i)

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n

RAor % =

i=1 n

RAos % =

i=1 n

qr =

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1  min(Ar , As(i) ) n max(Ar , As(i) )

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Ar − max(As(i) ) Ar

(5)

n

SimSize =

i=1

AFI =

Where n represents the number of segmented objects of interest, Ar is the area of the reference object, As(i) is the area of the ith segmented object, Ao(i) is the area of the ith overlapped region associated with the reference object, and Au(i) is the area of the union between the references object and the ith segmented object. When reference objects are well-segmented, both values of RAor and RAos are close to 100. The metric qr (Weidner 2008) ranges between 0 and 1. The values close to zero indicate a perfect match while values close to one indicate an over- or under-segmentation. The SimSize (Zhan et al. 2005) measures the similarity in terms of the size of the ith segmented object and ranges between 0 and 1, with one being ideal. If min(Ar , As(i) ) is Ar , then max(Ar , As(i) ) is As(i) and vice versa. As illustrated from (1) to (4), the metrics value are derived by the average of the segmented objects with the reference object because larger reference polygons or smaller scales parameter setting in eCognition may cause an interaction with more segments. AFI compares the area of the reference object with the largest area of the segmented objects. AFI is ideally zero, while AFI < 0 indicates under-segmentation and AFI > 0 denotes over-segmentation. Together with the above-mentioned area-based metrics, we also computed two location-based metrics: (6) position discrepancy of a segmented object to a reference object (Dsr ) and (7) relative position (RPsr ). Dsr =

n  2  2 1  Xs(i) − Xr + Ys(i) − Yr n i=1

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RPsr =

n 1

n

i=1

 2  2 Xs(i) − Xr + Ys(i) − Yr dmax

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Where Dsr is the average of the Euclidian distance (m) in the xy plane between the centroid coordinates of the ith segmented object (Xs(i) and Ys(i) ) and the centroid coordinates of the reference object (Xr and Yr ). Dsr tends towards 0 when the centres of objects are in the same location, while under- and over-segmentation produced increase of Dsr . RPsr is the average ratio of the Euclidian distance to the maximum distance (d max ) between the centre of reference object to the most distant segmented object. RPsr ranges between 0 and 1, with lower values being preferable. We implemented the metrics described above in the Python 2.7 environment (van Rossum and Drake 2001) using open-source libraries, OGR (http://www.gdal.org/ ogr/) and Shapely (https://pypi.python.org/pypi/Shapely). The code was compiled into an executable form and the Segmentation Accuracy Tool software (beta version) for Windows OS 64-bit is available free upon request to the authors.

3. Results and discussion The 57 scale parameters setting (from 100 to 1500 with an interval of 25) has resulted in creation of a wide array of segmented objects varying in number and size (figure 1). The number of segments of interest (with an overlapped region > 0 ha) decreased from 47,588 (scale 100) to 1134 (scale 1500) and their average size increased from 0.12 ha (scale 100) to 17.49 ha (scale 1500) with increasing the scale parameters.

(a)

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(b)

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200 m

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Figure 1. An example of multi-scale image segmentation using the Fractal Net Evolution Approach (FNEA) of eCognition. The three images were segmented with the colour criterion of 0.4 (shape criterion of 0.6), compactness criterion of 0.8 (smoothness of 0.2) and segmentation scale of 200 (a), 650 (b) and 1500 (c). Notes: The polygons with yellow boundary (area ∼7.4 ha, class 2) represent the reference objects (e.g. LPIS parcel), and the polygons with red boundary are the segmented objects with centroids (red triangles) within the reference area. The yellow triangles are the centroids of the reference objects. The polygons are overlaid on the 0.5-m spatial resolution panchromatic (Pan) band of WorldView-2 (WV2) (DigitalGlobe).

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As reported by Massada et al. (2012), the relationship between the scale parameter and the number of segmented objects was described by a non-linear function. In our study, the sharp drop in number of segments was located between 100 and 600. The thresholding method proposed by Pu and Landry (2012) was applied to reduce spurious effects caused by intersections that represent a small proportion of the reference object or the segment. However, there is no rule of thumb to define precisely which method is appropriate. For example, Ke et al. (2010) suggest that a segmented object is considered in the analysis if the overlapped area is over 10% of the reference area. Zhan et al. (2005) and Clinton et al. (2010) proposed an approach that takes in account both the centroid and percentage of overlapped area between objects. In this analysis, due to the large size difference between reference polygons, better results were achieved considering only the centroid parameter. Figure 2(a) shows the evaluation of RAos and RAos at different scale parameters. The low RAor values and high RAos values generated by small scales indicate undersegmentation of the segmented objects, while large scales produced high RAor values and low RAos values indicating over-segmentation. The similarity between RAor and RAos occurs for class 1 at scale parameter 550 (RAor = 74% and RAos = 72%), for class 2 at scale 500 (RAor = 76% and RAos = 75%), for class 3 at scale 1175 (RAor = 77% and RAos = 73%) and for class 4 at scale 1500 (RAor = 72% and RAos = 73%). As reported by Ke et al. (2010), the similarity between RAor and RAos indicates the overall balance between over- and under-segmentation for each reference object. The balance values for all classes showed that objects from segmentation have good match with reference objects. However, the scale parameter where the balance occurs is functional of the size of the reference object. Therefore, this result supports the idea that in the accuracy analysis, the set of reference objects should be of similar size in order to understand the optimal setting parameters. The qr and SimSize are illustrated in figure 2(b). The qr for class 1 decrease with increasing scales until a minimum (0.42) was reached (scale 525), and then decrease at larger scales. The remaining classes tend to have a stable value of qr after scale 900 (class 2 and class 3) or a decrease until scale 1500 (class 4). A similar trend, but in the opposite direction, has been observed for SimSize. The AFI metric (figure 3(a)) for class 2 to class 4 showed a similar trend with values included in a range between 1 and –1 with 0 to indicate a theoretical perfect match. Class 1 after scale 400 showed a negative trend for all scales. These negative values of AFI denote under-segmentation for the reference object with small area at larger scales. These different results of class 1 could be explained by the fact that the reference polygons with small area are derived from a manual segmentation based on property line which often does not correspond to any real limit. Otherwise, the reference polygons with medium and large area are often delimited by clear boundaries (e.g. hedgerow, ponds, ditches and road) that drive the segmentation algorithm after a certain scale parameter. In the presence of clear boundaries, the scale parameters, after a certain value, became less influent to create the boundaries of a segmented object. On the other hand, at smaller scales, a strong influence of the soil textures was observed on the creation of the segmented objects (figure 1(a)). Therefore, to further improve the segmentation, new analysis including lidar (light detection and ranging)-based segmentation and integration of spectral and lidar data are needed. The distance (figure 3(b)) between the centroids of segmented objects and the centroids of reference objects decreased with increasing scales until a minimum was reached (34.8 m at scale 600), and then slightly increased at larger scales. The Dsr values for class 2 to class 4 decreased from small scales to larger scales until certain scale values (800 for class 2, 900 for class 3 and 1050 for class 4); after that the values

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Figure 2. Segmentation quality evaluation using (a) relative area of an overlapped region to a reference object (RAor , black line) and the relative area of an overlapped region to a segmented object (RAos , red line), and using (b) quality rate (qr, black line) and the SimSize (red line).

remain constant. The larger value of Dsr at small scale is due to over-segmentation, where a single reference object is overlapped by a large number of segmented objects. While, higher values of Dsr at larger scales are due to class 1 polygons within the larger segment units produced by under-segmentation. The stability showed by the other classes may be confirming the influence of the clear boundaries to drive the FNEA of eCognition. The results of RPsr (figure 3(b)) showed a trend towards one for all classes, which means that the distance between the centroids of the segmented and reference objects was not very close. However, this can be explained by the presence of several reference objects with RPsr equal to one. This situation occurs even in the cases of well segmentation but with only a segmented object to interact with the reference object. When this event occurs, the Euclidean distance is equal to d max with consequence one as result. Ke et al. (2010) report that the Dsr could alone represent the positional accuracy of segmented object. Therefore, additional metrics are expected to be proposed and developed in order to improve the assessment of quality. Although these metrics were already applied to other studies, the segmentation quality assessment is a process that is still at initial stage. This is particularly true since, in most cases, the optimal

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Figure 3. Segmentation quality evaluation using (a) Area Fit Index (AFI), and using (b) position discrepancy of a segmented object to a reference object (Dsr , black line) and relative position (RPsr , red line).

segmentation parameters are still chosen by trial-and-error tests. For example, further research might include metrics related to the comparison of the shape geometry and the distance between the boundaries of the objects. Another consideration is although the image segmentation and the accuracy assessment were conducted automatically with separate software, the integration of these two tasks into a single procedure could be worthy of further investigation. Finally, although several segmentation algorithms and techniques have been proposed for the segmentation of remotely sensed data, they are not specifically conceived for the segmentation of the farmer land parcels. Further analyses in order to develop new segmentation algorithms specified for the delineation of the farmer field parcels in the LPIS framework are needed. 4. Conclusions The results of this analysis have useful implications regarding the use of remotesensing segmentation techniques to delineate farmer land parcels in a Danish rural

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landscape. Although the eCognition software offers greater functionality, it is not a panacea for automatic and accurate extraction of features when the reference polygons are not delimited by clear boundaries. Several accuracy metrics are used in order to understand the optimal setting parameters of eCognition. Among these metrics, RAor , RAos , AFI and Dsr have provided the best interpretation in order to facilitate and assist the users to the identification of the optimal segmentation setting parameters. Additionally, the training set should be of similar size in order to understand the optimal setting parameters. In this way, using eCognition with shape factor assigned to 0.6 (and colour to 0.4) and compactness parameter to 0.8 (and smoothness to 0.2), the optimal segmentation scale parameters for the analysis conducted were 550 for class 1, 500 for class 2, 1175 for class 3 and 1500 class 4, respectively. Acknowledgements The authors would like to thank Ronald E. McRoberts and Piermaria Corona for their valuable comments and suggestions to improve the quality of the letter.

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