Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models

Autor: Daniel G. García-Murillo, J. Caicedo-Acosta, G. Castellanos-Dominguez
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Remote Sensing, Vol 12, Iss 10, p 1633 (2020)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs12101633
Popis: Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do.
Databáze: Directory of Open Access Journals
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