Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods

2001 ◽  
Vol 31 (5) ◽  
pp. 832-844 ◽  
Author(s):  
Juho Pitkänen
Keyword(s):  
Digital Camera ◽  
Adaptive Methods ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Image Smoothing ◽  
Individual Tree ◽  
Local Maxima ◽  
Tree Detection ◽  
Locally Adaptive ◽  
Tree Data

Locating local maxima of grey levels in aerial images was used for individual tree detection in boreal, closed forest conditions in southern Finland. Image smoothing and binarization were used as preprocessing steps. Binarization was used to restrict the local maxima searching to the bright areas of the images, which were assumed to be tree crowns. Because brightness variations are typical of aerial images, both within and among images, locally adaptive methods were suggested for binarization. Aerial digital camera images and mapped tree data of eight stands in three field plots were used. Four adaptive binarization methods were compared. Differences in tree detection accuracy were small even though the appearance of the binarized images were different. Image smoothing improved the results of tree detection in the three stands that had the largest mean tree size. Tree detection worked fairly well in all seven stands with a density of less than 1500 trees/ha. In these stands, 70–95% of the trees were detected, whereas only 54% were detected in the last stand, which had a density of approximately 1900 trees/ha.

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

2020 ◽  
Vol 12 (10) ◽  
pp. 1633 ◽  
Author(s):  
Daniel G. García-Murillo ◽  
J. Caicedo-Acosta ◽  
G. Castellanos-Dominguez
Keyword(s):  
Low Cost ◽  
False Negative ◽  
False Negative Rate ◽  
Seed Selection ◽  
Individual Tree ◽  
Local Maxima ◽  
Commission Errors ◽  
Tree Detection ◽  
Surface Models ◽  
Cumulative Summation

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.

Development and evaluation of an automated tree detection–delineation algorithm for monitoring regenerating coniferous forests

2005 ◽  
Vol 35 (10) ◽  
pp. 2332-2345 ◽  
Author(s):  
D A Pouliot ◽  
D J King ◽  
D G Pitt
Keyword(s):  
Absolute Error ◽  
Tree Regeneration ◽  
Tree Size ◽  
Detection Accuracy ◽  
Individual Tree ◽  
Commission Errors ◽  
Tree Detection ◽  
Crown Diameter ◽  
Accuracy Index ◽  
The Individual

An algorithm is presented for automated detection–delineation of coniferous tree regeneration that combines strategies of several existing algorithms, including image processing to isolate conifer crowns, optimal image scale determination, initial crown detection, and crown boundary segmentation and refinement. The algorithm is evaluated using 6-cm pixel airborne imagery in operational regeneration conditions typically encountered in the boreal forest 5–10 years after harvest. Detection omission and commission errors as well as an accuracy index combining both error types were assessed on a tree by tree basis, on an aggregated basis for each study area, in relation to tree size and the amount of woody competition present. Delineation error was assessed in a similar manner using field-measured crown diameters as a reference. The individual tree detection accuracy index improved with increasing tree size and was >70% for trees larger than 30 cm crown diameter. Crown diameter absolute error measured from automated delineations was <23%. Large crown diameters tended to be slightly underestimated. The presence of overtopping woody competition had a negligible effect on detection accuracy and only reduced estimates of crown diameter slightly.

Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners

2021 ◽  
Vol 13 (1) ◽  
pp. 1028-1039
Author(s):  
Midhun Mohan ◽  
Rodrigo Vieira Leite ◽  
Eben North Broadbent ◽  
Wan Shafrina Wan Mohd Jaafar ◽  
Shruthi Srinivasan ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Low Cost ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Local Maxima ◽  
Tree Detection ◽  
High Resolution Imagery ◽  
Lm Algorithm

Abstract Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpretation from high-resolution imagery and field-data-based accuracy assessment. As the intended audience are beginners in remote sensing, we have adopted a very simple methodology and chosen study plots that have relatively open canopies to demonstrate our proposed approach; the respective R codes and sample plot data are available as supplementary materials.

scholarly journals INDIVIDUAL TREE DETECTION FROM UAV LIDAR DATA IN A MIXED SPECIES WOODLAND

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 657-663 ◽  
Author(s):  
A. Zaforemska ◽  
W. Xiao ◽  
R. Gaulton
Keyword(s):  
Point Cloud ◽  
Voronoi Tessellation ◽  
Mean Shift ◽  
Region Growing ◽  
Single Species ◽  
Mixed Species ◽  
Individual Tree ◽  
Local Maxima ◽  
Tree Detection ◽  
Segmentation Algorithms

<p><strong>Abstract.</strong> The study evaluates five existing segmentation algorithms to determine the method most suitable for individual tree detection across a species-diverse forest: raster-based region growing, local maxima centroidal Voronoi tessellation, point-cloud level region growing, marker controlled watershed and continuously adaptive mean shift. Each of the methods has been tested twice over one mixed and five single species plots: with their parameters set as constant and with the parameters calibrated for every plot. Overall, continuous adaptive mean shift performs best across all the plots with average F-score of 0.9 with fine-tuned parameters and 0.802 with parameters held at constant. Raster-based algorithms tend to achieve higher scores in coniferous plots, due to the clearly discernible tops, which significantly aid the detection of local maxima. Their performance is also highly dependent on the moving size window used to detect the local maxima, which ideally should be readjusted for every plot. Crown overlap, suppressed and leaning trees are the most likely sources of error for all the algorithms tested.</p>

Optimizing individual tree detection accuracy and measuring forest uniformity in coconut (Cocos nucifera L.) plantations using airborne laser scanning

2019 ◽  
Vol 409 ◽  
pp. 108736 ◽  
Author(s):  
Midhun Mohan ◽  
Bruno Araujo Furtado de Mendonça ◽  
Carlos Alberto Silva ◽  
Carine Klauberg ◽  
Acauã Santos de Saboya Ribeiro ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Cocos Nucifera ◽  
Airborne Laser Scanning ◽  
Detection Accuracy ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Cocos Nucifera L

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

scholarly journals MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1426
Author(s):  
Chuanyang Liu ◽  
Yiquan Wu ◽  
Jingjing Liu ◽  
Jiaming Han
Keyword(s):  
Feature Detection ◽  
Feature Fusion ◽  
Memory Storage ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Composite Insulator ◽  
Running Time ◽  
Scale Feature ◽  
Multi Scale ◽  
Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

Road Detection Based on Edge Feature with GAC Model in Aerial Image

2021 ◽  
Author(s):  
Linying Zhou ◽  
Zhou Zhou ◽  
Hang Ning
Keyword(s):  
Transformation Method ◽  
Aerial Images ◽  
Aerial Image ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Road Detection ◽  
Canny Operator ◽  
The Road ◽  
Gradient Magnitude ◽  
Edge Feature

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

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