Palm Tree Detection Using Circular Autocorrelation of Polar Shape Matrix

Abstract

Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape matrix representation of an image, as the shape feature, and the linear support vector machine to standardize and reduce dimensions of the feature. Finally, the study uses local maximum detection algorithm on the spatial distribution of standardized feature to detect palm trees. The method was applied to 8 images chosen from different tough scenarios and it performed on average with an accuracy of 84% and 76.1%, despite being subjected to different challenging conditions in the chosen test images.

Finding complex patterns using template matching

“…Results are also heavily influenced by the testing method, and of course, the nature and quality of the test images. Nevertheless, it is noteworthy that in spite of the conceptual simplicity of our approach, the detection quality achieved here for tree detection lies well within the range of other published work on the detection of palm trees (Malek, Bazi, Alajalan, AlHichric and Melgani, 2014;Srestasathiern & Rakwatin, 2014;Manandhar, Hoegner & Stilla, 2016). While we have set the threshold thr r in this study to achieve a low and balanced error rate, software-supported workflows will typically warrant a different type of threshold adjustment.…”

Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation

“…Emerging work by Malek et al (2014) and Manandhar, Hoegner, and Stilla (2016) indicates the potential of computer vision and object based detection for automated oil palm identification and counting. Further work is needed to develop and demonstrate the robustness of these methods in complex plots with larger undergrowth.…”

Characterization and Estimation of Dates Palm Trees in an Urban Area Using GIS-Based Least-Squares Model and Minimum Noise Fraction Images

“…Moreover, the machine learning-based methods were applied for the palm tree detection by using a scale-invariant feature transform (SIFT, and unmanned aerial vehicle (UAV) images (Malek et al 2014) The circular autocorrelation of the polar shape matrix and a linear support vector machine were used to reduce the feature dimensions for detecting date palm (Manandhar et al 2016). As follows, remote sensing is a valuable tool for implementing early discovery and constant observation of many difficulties (Chong et al 2017).…”