Tree crown detection and delineation in satellite images using probabilistic voting

Ozcan, Abdullah H.; Hisar, Dilara; Sayar, Yetkin; Ünsalan, Cem

doi:10.1080/2150704x.2017.1322733

Cited by 19 publications

(17 citation statements)

References 25 publications

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“…The authors showed that an object-based approach achieved higher accuracy than a pixel-based approach in the detection and delineation rates. Özcan, et al [26] used probabilistic voting for crown detection and watershed segmentation for crown delineation in order to help forecasting the crop yield in an olive, peach, pear and citrus orchard using Google Earth images. Enumeration and delineation of oil palm trees from high-resolution satellite imagery has also received important attention [27,28].…”

Section: Introductionmentioning

confidence: 99%

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Csillik

Cherbini²,

Johnson

et al. 2018

Drones

173

116

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Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

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Section: Introductionmentioning

confidence: 99%

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Csillik

Cherbini²,

Johnson

et al. 2018

Drones

173

116

View full text Add to dashboard Cite

show abstract

“…Therefore, when considering UAV imagery, it is possible to acquire information more efficiently and inexpensively [9]. UAV imagery have been used to estimate metrics from trees in several crops or farms, which can be of interest of farmers in order to obtain a better planning of the irrigation process, helping in the forecast of the production date and climate change effects as a consequence of deforestation and degradation of forests, which leads to global warming, precision forestry, forest management planning, and biomass estimation [2,[10][11][12][13]. Most of the work conducted with UAV data has been related with forest areas [11,12,[14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…However, in the field of agriculture crops, it is relevant to study the geometric characterization of trees and plantations. This is a complex task, being particularly important to understand the tree growth and productivity [1,9,13,[20][21][22]. In the field of precision agriculture, several image processing methods for automatic detection and delineation of individual trees were already implemented [1].…”

Section: Introductionmentioning

confidence: 99%

“…The information regarding tree crown areas of a specific crop is the key in the study of ecological value and ratio of the occupied area by that crop [9,15,23]. Traditionally, tree counting has been performed manually based on field information and statistical data, being an expensive process, time consuming, and a tedious task subject to several errors [13,14,22]. Satellite, manned aircraft, or UAV imagery can be used to obtain these data.…”

Section: Introductionmentioning

confidence: 99%

“…Satellite, manned aircraft, or UAV imagery can be used to obtain these data. Therefore, the implementation of automatic processes is crucial to extract information and provide forest or crops inventories from remote sensing data [2,13,22]. Several studies have been developed with different techniques to estimate the tree crown delineation, tree counting, and tree characterization from remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs

Duarte

Teodoro

2018

IJGI

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Unmanned Aerial Vehicle (UAV) imagery allows for a new way of obtaining geographic information. In this work, a Geographical Information System (GIS) open source application was developed in QGIS software that estimates several parameters and metrics on tree crown through image analysis techniques (image segmentation and image classification) and fractal analysis. The metrics that have been estimated were: area, perimeter, number of trees, distance between trees, and a missing tree check. This methodology was tested on three different plantations: olive, eucalyptus, and vineyard. The application developed is free, open source and takes advantage of QGIS integration with external software. Several tools available from Orfeo Toolbox and Geographic Resources Analysis Support System (GRASS) GIS were employed to generate a classified raster image which allows calculating the metrics referred before. The application was developed in the Python 2.7 language. Also, some functions, modules, and classes from the QGIS Application Programming Interface (API) and PyQt4 API were used. This new plugin is a valuable tool, which allowed for automatizing several parameters and metrics on tree crown using GIS analysis tools, while considering data acquired by UAV.

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Probabilistic object detection and shape extraction in remote sensing data

Ozcan¹,

Ünsalan

2020

Computer Vision and Image Understanding

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Tree crown detection and delineation in satellite images using probabilistic voting

Cited by 19 publications

References 25 publications

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs

Probabilistic object detection and shape extraction in remote sensing data

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