Tree height measurement based on image processing with 3-points correction

Han, Dianyuan

doi:10.1109/iccsnt.2011.6182429

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Work (Juan & Xin-yuan, 2009), (Dianyuan & Chengduan, 2011) adopted a similar idea for obtaining the height of trees, which is calculated via a proportion transform of the coordinates of three pre-set makers on the tree. The extension to the system was presented in (Dianyuan, 2011), which used three marker points and a perspective transformation. Essentially, the principal idea of these approaches is to transfer the task of measuring the plant height to the task of locating pre-set markers using image processing algorithms.…”

Section: Grass Biomass Estimationmentioning

confidence: 99%

Density Weighted Connectivity of Grass Pixels in image frames for biomass estimation

Li-gang¹,

Verma²,

Stockwell³

et al. 2018

Expert Systems with Applications

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Accurate estimation of the biomass of roadside grasses plays a significant role in applications such as fire-prone region identification. Current solutions heavily depend on field surveys, remote sensing measurements and image processing using reference markers, which often demand big investments of time, effort and cost. This paper proposes Density Weighted Connectivity of Grass Pixels (DWCGP) to automatically estimate grass biomass from roadside image data. The DWCGP calculates the length of continuously connected grass pixels along a vertical orientation in each image column, and then weights the length by the grass density in a surrounding region of the column. Grass pixels are classified using feedforward artificial neural networks and the dominant texture orientation at every pixel is computed using multiorientation Gabor wavelet filter vote. Evaluations on a field survey dataset show that the DWCGP reduces Root-Mean-Square Error from 5.84 to 5.52 by additionally considering grass density on top of grass height. The DWCGP shows robustness to non-vertical grass stems and to changes of both Gabor filter parameters and surrounding region widths. It also has performance close to human observation and higher than eight baseline approaches, as well as promising results for classifying low vs. high fire risk and identifying fire-prone road regions.

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Section: Grass Biomass Estimationmentioning

confidence: 99%

Density Weighted Connectivity of Grass Pixels in image frames for biomass estimation

Li-gang¹,

Verma²,

Stockwell³

et al. 2018

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…For challenge (3), existing research on building height estimation based on street view images is very limited. Most scholars (W. Ding et al, 2008;Han, 2011;Yuan & Cheriyadat, 2016) have calculated the height of some objects through normal images based on geometric relationships. For instance, Al-Habashna (2020) and Díaz and Arguello (2016) proposed the algorithms for estimating the heights of buildings using single view images in the street view map.…”

Section: Introductionmentioning

confidence: 99%

Building height calculation for an urban area based on street view images and deep learning

Zhang

et al. 2022

Computer aided Civil Eng

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The building heights of an urban area are useful for space analysis, urban planning, and city management. To this end, a novel method for building height calculation for an urban area is proposed based on street view images and a deep learning model, that is, mask region-based convolutional neural network (Mask R-CNN). First, a spider of street view maps was developed, and an optimization model for observation locations was designed based on a genetic algorithm, by which the street view images of all buildings can be obtained with the minimum number of downloads. Subsequently, a deep learning workflow was designed based on the Mask R-CNN to detect buildings from the panorama images.Finally, an accurate height calculation model considering repeated detection of buildings was developed by mapping between detected buildings and actual buildings. Case studies indicate that the mean error of height calculation is 0.78 m, which achieves high precision for calculating building heights in urban areas, while the average calculation time is 4.57 s per building, which indicates that the proposed method is efficient for the application in urban areas.

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