Development of young oil palm tree recognition using Haar- based rectangular windows

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1 IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS Development of young oil palm tree recognition using Haar- based rectangular windows To cite this article: S Daliman et al 2016 IOP Conf. Ser.: Earth Environ. Sci View the article online for updates and enhancements. This content was downloaded from IP address on 09/04/2018 at 02:35

2 Development of young oil palm tree recognition using Haarbased rectangular windows S Daliman 1, S A R Abu-Bakar 1, S H Md Nor Azam 2 1 Computer Vision, Video and Image Processing (CvviP) Research Lab, Department of Electronics and Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, MALAYSIA. 2 Sime Darby Research Sdn. Bhd., Jalan Pulau Carey, Pulau Carey, Selangor, MALAYSIA. 1 shaparas@utm.my Abstract. This paper presents development of Haar-based rectangular windows for recognition of young oil palm tree based on WorldView-2 imagery data. Haar-based rectangular windows or also known as Haar-like rectangular features have been popular in face recognition as used in Viola-Jones object detection framework. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palm tree. A set of seven Haar-based rectangular windows have been designed to better match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Determination of features for oil palm tree is an essential task to ensure a high successful rate of correct oil palm tree detection. Furthermore, features that reflects the identification of oil palm tree indicate distinctiveness between an oil palm tree and other objects in the image such as buildings, roads and drainage. These features will be trained using support vector machine (SVM) to model the oil palm tree for classifying the testing set and subimages of WorldView-2 imagery data. The resulting classification of young oil palm tree with sensitivity of 98.58% and accuracy of 92.73% shows a promising result that it can be used for intention of developing automatic young oil palm tree counting. 1. Introduction Malaysia has a vast area of oil palm plantation. The current standard of monitoring the number of oil palm trees have been either manually counting by deploying human worker at the plantation itself or by manually counting from the given airborne images. With high resolution of 2m in WorldView-2 images, these images can be further enhanced by using Haar-based rectangular windows to extract information beneficial for farm monitoring purposes, such as tree detection. The purpose of detecting oil palm tree is to locate or count the oil palm trees based on satellite imagery of WorldView-2. This information is the key factor for oil palm plantation management and monitoring in each plantation area, where yield prediction can be obtained by knowing the numbers of oil palm tree. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd 1

3 Haar-based rectangular features also known as Haar-like rectangular features wass firstly introduced by [1]. The name of Haar is intuitively similar too Haar wavelets and these Haar-basedd rectangularr features have been used for face recognition in Viola-Jones object detectionn framework as the first real-time face recognition [2]. Viola-Jones algorithm have becoming popular for face detection since then. Haar-based rectangular features particularly Viola-Jones algorithm has been widely experimented for other object recognition and detection as well. Recently, Haar-based rectangular featuress or also known as Haar-based rectangular windows have also been used for pedestrian tracking [3], stop sign detection [4] and mostly for other features in faces, such as eyes and mouth detection. Thus, this study will use the Haar-based rectangular windowss for features extraction of oil palm tree. 2. Feature extraction and oil palm tree recognition 2.1. Haar-based rectangular windows Haar-based rectangular windowss used in [2] are based on Haar wavelets which are single wavelength square waves. In two dimensionss image, a square wave is a pair of adjacent rectangles with one-sided light pixels and another one sided having dark pixels. The rectangles used for face detection are not truly Haar wavelets. The rectangle combinations used in face recognition is designed for better suit to visual recognition tasks. Thus, these rectangles are usually referred to as Haar-based rectangular windows, or Haar-like features, rather r than wavelets. The presence of a Haar feature is determinedd by subtracting the average dark-region pixel value from the average light-region pixel value. If thee difference is above a threshold which was set during learning algorithm, that feature is i said to be present [2]. This binary determination is implemented to identify as face or not a face. Haar features can be described as a simple and inexpensive image features based on intensity differences between rectangle-based regions that sharee similar shapes to the Haar wavelets [5], which will be defined as: F Ha aar = E (R white e) E (R black ) (1) where R white refers to the light region pixels of an image and R black refers to the dark region pixels of an image. However, some images of the same object may have differentt monotonic illuminationn changes due to the light differences. Thus, Haar features as defined in Equation 1 havee to be normalized as follows: FHaar E( R E( R white white ) E( R ) 2 E( R black ) black ) 2 (2) In addition to the application of Haar-based rectangular window, an integral image computation need to be done for fast feature calculation. The Haar-based rectangular features can be computed very fast and in constant time for any size by means of two auxiliary images [6]. The auxiliary image is the Summed Area Table (SAT) which is definedd as the sum of the pixels of the upright rectanglee ranging from the top left corner at (0,0) to the bottom right cornerr at (x,y) as inn Figure 1a (a) (b) Figure 1. Summed Area Table for fast computation off Haar features [6]. 2

4 Summed Area Table, SAT(x,y) can be stated d as: SAT x, y x' x, y' y Ix ', y' (3) SAT(x,y) ) can be calculated with one pass over all pixels from left to right and top to bottom by modifying Equation 3.25 into: SAT x, y SAT x, y 1 SAT x 1, y I x, y SATS x 1, y 1 (4) where SAT 1, y SATx, 1 0 (5) Based on Equation 5, the sum of pixels in Haar-based rectangular window can be e computed using four lookups table as in Figure 1b and can be defined as: RecSumr SATx 1, y SATS x w 1, y h SAT x 1, y h 1 SATT x w1, y 1 (6) 1 where h and w refers the heightt and width of the Haar-based on young oil palm tree and non-oil palm tree rectangular window, respectively. For computing the Haar-based rectangular windows database, Equation 6 will be implemented for calculating SAT of oil palm tree and non-oil palm tree features. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palmm tree. Evaluation of Haar feature based on Equation 2 will be considered as WorldView-2 windows, namely as hf1, hf2, hf3, hf4, hf5, hf6 and images tend to have different illumination changes. A set of seven Haar-based rectangular hf7 have been designed to betterr match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Illustrations of Haar-based rectangular r windows used for oil palm tree recognition are depicted in Figure 2, while Figure 3 shows samples off young oil palm tree with each of Haar-based rectangular windows. 1 Figure 2. Haar-based rectangular windows for oil palmm tree recognition. Figure 3. Samples of young oil palm tree with Haar-basedd rectangular r windows. 3

5 The Haar-based rectangular windows are also designedd due to factt that the young oil palm tree in WorldView-2 image has a significantly low contrast compared to its surrounding area. As shown in Figure 4, these seven Haar-based rectangular windows aim att characterizing severall distinct descriptors of young oil palm tree in a 25x25 pixels window. This is i because some of the young oil palm trees are located near to the drainage or r land banks. Figure 4. Seven Haar-based rectangular windows on different characteristics of young oil palm tree Support vector machine The support vector machine (SVM) classifierr will be considered in this study. This optimal separating hyperplane of support vectors [7]] can be described based in Figure 5 below. b Figure 5. The optimal separating hyperplane of support vectors [7]. The foundations of support vector machines have been developedd by [8]. The support vectors as illustrated in Figure 5 are the data points that the margin pushes up against in creating the maximum margin linear classifier [7], which in this case to classify the data into two categories of + 1 and -1. This is the simplest kind of SVM that is also known as linear SVM. The hyperplane, w, x b in Figure 5 can be defined as [9] w, x b 0 (7) 4

6 The margin, can be denoted as, min :, ; min :, ; min :, min :, min :, min :, (8) The linear SVM needs to be developed in assessing issues linear constraints by optimizing a quadratic function. By constructing a dual problem that involves Lagrange multiplier, i, the classifying function will have the form of [7] (9) The linear classifier is based on the dot product between vectors as in Equation 10 as follows, (10) In solving the non-linear classification, every data point needs to be mapped into high-dimensional space through transformation as follows Φ: (11) and the dot product in Equation 10 will becomes the kernel function as follows, (12) The kernel function is a function that corresponds to an inner product of expanded feature [7]. Several different types of kernel function such as linear, polynomial and Gaussian kernel functions can be generated by modifying the Equation 12. The linear kernel function is defined as: and the polynomial of power, p kernel function is denoted as, (13), 1 (14) The Gaussian kernel function or also known as radial-basis function network [7] is defined as:, (15) The classifying function in Equation 9 will be modified for the kernel functions as follows: (16) In this study, the kernel function of linear has been used for the young oil palm tree recognition. 5

7 3. Methodology 3.1. Study area and data collection A database of 561 young oil palm trees andd 214 matured oil palm trees has been selected from the WorldView-2 imagery data. The coordinate of each oil palm tree is recorded based on the center of the tree crown visible in the WorldView-2 image. The distribution of selected young and matured oil palm tree is depicted as in the following Figure 6, while Figure 7 depicted the example of a true colour WorldView-2 image of a matured and a young oil palm tree. Figure 6. Distribution locations of young oil palm trees (blue) and matured oil palm trees (green). Figure 7. Samples of matured and young oill palm trees in true colour of WorldView-2 imagery data. By comparing matured and young oil palm tree as in Figure 7, the difference in ages of oil palm tree is clearly visible. The crown for matured oil palm tree looks like a flower petals, whilst the crown for the young oil palm treee looks darker and circular-shaped with its size is much smaller. Thesee features would be essential value for features extraction of young oil palm treee recognition Calculation of oil palm tree features Features for young oil palm trees are computed using the seven Haar-based H rectangular windows. Location of the seven Haar-based rectangular windows are illustrated individually in Figure 8 for sample of young oil palm tree. Figure 8. Seven Haar-based rectangular r windows individually on sample of young oil palm tree. 6

8 4. Results and discussion A database of 561 young oil palm trees, 214 matured oil palm trees, and also 761 points of non-oil palm tree objects have been selected from the WorldView-2 imagery data. These data were divided into two parts: one for training and another one for testing set as shown in Table 1. Table 1. Distribution of training and testing set for oil palm tree classification. Oil Palm Train Set Test Set Total Points Young Oil Palm Tree Matured Oil Palm Tree Total Oil Palm Tree Non-Oil Palm Objects Train Set Test Set Total Points Water body Roads Buildings Open Area/Field Ground/Background of oil palm tree Total Non-Oil Palm Tree Objects The performance of each features class were evaluated by performing classification on the testing set. Each features set were measured by classification accuracy and sensitivity which defined as [10]: C Classification accuracy = * T P Sensitivity= * Tp (17) (18) where C represents a total of correctly classified data, T is a total of test data, P representing a total of correctly classified oil palm tree and Tp representing a total of tested oil palm tree. The classification accuracy is used to represent overall classification performance while sensitivity represents the capability of classification system to identify the oil palm tree. The latter indicator is also used to represent the capability of oil palm tree recognition model to be developed. Sensitivity is important to ensure high capability of oil palm tree recognition model to identify the oil palm tree correctly and accurately. High sensitivity ensures good performance in automated oil palm tree counting. Features of oil palm tree were computed based on Haar-based rectangular windows by implementing hf1, hf2, hf3, hf4, hf5, hf6 and hf7 windows on the database of oil palm and non-oil palm tree objects. Classification of young, matured and non-oil palm tree using Haar-based rectangular windows have been implemented using SVM. Results of classification for band 1, band 2, band 3 and band 4 of WorldView-2 imagery data are shown in Table 2. Based on the accuracy and sensitivity results for each features derived Haar-based rectangular windows, the best features was obtained using band 4 of WorldView-2 image with 98.58% and 98.13% sensitivity for young and matured oil palm tree respectively, and overall accuracy of 92.73%. Higher accuracy and better sensitivity response from Haar-based rectangular windows may result from the selection of Haar features that match with the specifications of the oil palm tree especially the young oil palm tree with the highest sensitivity performance. 7

9 Table 2. Results of oil palm tree classification using Haar-based rectangular windows for band 1, 2, 3 and 4 of WorldView 2 image. Class of Band 1 Band 2 Band 3 Band 4 Classification Young Oil Palm Tree Matured Oil Palm Tree Water body 8th IGRSM International Conference and Exhibition on Remote Sensing & GIS (IGRSM 2016) IOP Publishing True False Positive Positive Sensitivity 98.58% 96.26% True False Sensitivity Positive Positive % % 15 0 True False Positive Positive Sensitivityy 97.86% 97.20% True Positive False Sensitivity Positive P % % 0 Roads Buildings % Open Area/ /Field Ground/ Background of oil palm tree Accuracy % 67.53% 90.52% % % % % % % % % % The oil palm tree detection has been implemented by using oil palmm tree recognition model based on Haar-based rectangular windows. Two testedd 100x100 pixels subimage for young oil palm tree have been used to determine the effectiveness off the model. Distance off horizontal and vertical between location of the first three detectedd oil palm tree have been implemented to identify the existence of the tree, as the locations of young oil palm tree can be predicted based on the first three locations of oil palm tree detected as shown in Figure 9 and 10. In Figure 9, all predicted p locations were detected leading to accuracy of young oil palm tree detected. Furthermore, Figure 100 shows the ability of the young oil palm tree recognition model too achieve accuracyy of detectedd tree and signify the location of non-oil palm tree object as indicated. This proves that oil palm tree recognition model gives high performance and promising result for identifying and counting young oil palm tree. Figure 9. Young oil palm tree detection with accuracy. Figure 10. Young oil palm tree detection with accuracy and succeed in detecting non-oil palm tree object. 8

10 5. Conclusions The classification of young, matured and non-oil palm tree have been performed. As a result, it is found that features obtained from Haar-based rectangular windows has achieved 92.73% overall accuracy with 98.58% sensitivity to young oil palm tree classification. Implementation of oil palm tree detection using oil palm tree recognition model based on Haar-based rectangular windows also have shown promising result for identifying and counting the young oil palm tree. Thus, it can be concluded that oil palm tree features derived from Haar-based rectangular windows is the most suitable for oil palm tree recognition model. 6. References [1] Papageorgiou C P, Oren M and Poggio T 1998 A general framework for object detection IEEE Sixth International Conference on Computer Vision pp [2] Viola P and Jones M 2001 Rapid object detection using a boosted cascade of simple features Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001 vol 1 pp [3] Viola P, Jones M J and Snow D Detecting pedestrians using patterns of motion and appearance International Journal of Computer Vision, 63(2), pp [4] Brkic, K., An overview of traffic sign detection methods. Department of Electronics, Microelectronics, Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Unska, 3, p [5] Gerónimo D 2009 Haar-Like Features and Integral Image Representation Universitat Autónoma de Barcelona [6] Lienhart R and Maydt J 2002 An extended set of Haar-like features for rapid object detection Proceedings of IEEE International Conference on Image Processing vol 1 pp [7] Tan M 2004 Support vector machine & its applications The University of British, Columbia [8] Vapnik V N 1995 The nature of statistical learning theory New York: Springer-Verlag [9] Gunn S R 1998 Support Vector Machines for Classification and Regression Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton [10] Liang H and Hartimo I 1998 A heart sound feature extraction algorithm based on wavelet decomposition and reconstruction Proceedings of IEEE 20th Annual International Conference of the Engineering in Medicine and Biology Society vol 3 pp Acknowledgments The authors would like to thank Sime Darby for providing the WorldView-2 imagery data which operated by DigitalGlobe. Authors would also like to express appreciation to the members of CvviP for their support and valuable comments and ideas. Authors also acknowledge the Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia (through research grant Q.J H22) for providing financial support in completing this study. 9