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

8th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Daliman, S.^1 ; Abu-Bakar, S.A.R.^1 ; Md Nor Azam, S.H.^2

Computer Vision, Video and Image Processing (CvviP) Research Lab, Department of Electronics and Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor

81310, Malaysia^1

Sime Darby Research Sdn. Bhd., Jalan Pulau Carey, Pulau Carey, Selangor

42960, Malaysia^2

关键词: Crown size;    Detection framework;    Imagery data;    Oil palm tree;    Rectangular windows;    Subimages;    Viola jones;    Worldview-2;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/37/1/012041/pdf
DOI  :  10.1088/1755-1315/37/1/012041

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.