Palm Tree Detection Using Circular Autocorrelation of Polar Shape Matrix

Manandhar, A.; Hoegner, L.; Stilla, Uwe

doi:10.5194/isprsannals-iii-3-465-2016

Cited by 6 publications

(3 citation statements)

References 7 publications

(9 reference statements)

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“…Validation site 4 was particularly challenging for palm tree detection because the palm trees were mixed with or even partly covered by other tree species. Although the accuracy rate of 94.63% was not as good as those at the other three sites, it was still satisfactory and higher than the accuracy rates reported in previous studies (Malek et al, 2014;Manandhar et al, 2016). This complicated situation is not very common and exists only on the boundaries between palm tree farmland and natural forestland.…”

Section: Resultscontrasting

confidence: 59%

“…The overall accuracy rates were 99.21% for the training site and 99.39%, 99.06%, 99.90% and 94.63% for the four testing sites, respectively. Even for the most challenging site, the accuracy of palm tree detection was still higher than that reported for the two methods proposed by Malek et al (2014) and Manandhar et al (2016), respectively. The principle of this method is relatively simple, so it has great potential for palm tree detection over large areas.…”

Section: Conclusion and Discussionmentioning

confidence: 55%

“…However, their experimental areas presented relatively easy cases for palm tree detection because the palm trees were sparsely distributed without canopy overlapping and differed significantly in appearance from the bare soil background. Manandhar et al (2016) noted that in dense cultivation regions, the method proposed by Malek et al (2014) would have difficulty finding the arbitrary boundaries between the overlapped palm trees. Therefore, they proposed a new method to detect and count palm trees.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of individual oil palm trees from UAV images using HOG features and an SVM classifier

Wang

Zhu

2018

International Journal of Remote Sensing

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Oil palm trees are important economic crops in tropical areas. Accurate knowledge of the number of oil palm trees in a plantation area is important to predict the yield of palm oil, manage the growing situation of the palm trees and maximise their productivity. In this study, we propose a novel automatic method for detection and enumeration of individual oil palm trees using images from unmanned aerial vehicles. This method required three major steps. First, images from unmanned aerial vehicles were classified as vegetation or non-vegetation by the support vector machine classifier. Second, a feature descriptor based on the histogram of oriented gradient was designed for palm trees and used to extract features for machine learning. Finally, a support vector machine classifier was trained and optimised using the histogram of oriented gradient features from positive (i.e., oil palm trees) and negative samples (i.e., objects other than oil palm trees). The trained classifier was then applied to detect individual oil palm trees using adaptive moving windows that allowed it to also return the crown size of each oil palm tree. The method was trained at one site and validated independently at four other sites with different situations. The overall accuracy of palm tree detection was 99.21% at the training site and 99.39%, 99.06%, 99.90% and 94.63% at the four validation sites; the last one was for the most challenging site, in which palm trees were mixed with other trees. These tests confirm the effectiveness of the proposed method. The simplicity and great efficiency of the proposed method allow it to support oil palm tree counting for large areas using imagery from unmanned aerial vehicles.

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Section: Resultscontrasting

confidence: 59%

Section: Conclusion and Discussionmentioning

confidence: 55%