Evaluation of UAV imagery for mapping <i>Silybum marianum</i> weed patches

Tamouridou, Afroditi Alexandra; Alexandridis, Thomas; Pantazi, X-E.; Lagopodi, Anastasia L.; Kashefi, J.; Moshou, Dimitrios

doi:10.1080/01431161.2016.1252475

Cited by 44 publications

(46 citation statements)

References 23 publications

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“…The geometric calibration of the cameras was performed, by taking some images of a b/w planar calibration grid of known geometric properties; the parameters of a Brown distortion model [26] were estimated through PhotoModeler Scanner [27]. Moreover, the CIR dataset was radiometrically corrected as recommended by the ADC Lite technical documentation [18]. Some images were taken of a small white Teflon calibration plate, provided with the camera, whose spectral response is known.…”

Section: Photogrammetric Processingmentioning

confidence: 99%

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

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This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.

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Section: Photogrammetric Processingmentioning

confidence: 99%

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

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“…Using machine learning algorithms (support vector machines) it was possible to achieve high accuracies of mapping broadleaved weeds in maize and sunflower fields even using RGB images acquired from a UAV [10]. The features offering the higher discrimination capacity were selected from a set of several statistics and measures of different nature, and the resulting map achieved an accuracy as high as 95.5%.With the increasing availability of very-fine-resolution UAV imagery, the question of optimum spatial resolution has been examined in weed mapping [8,11]. While mapping the early stages of broadleaved weeds, spatial resolution better than 2 cm had higher performance than 4 cm [7].…”

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confidence: 99%

“…However, the highest resolutions did not necessarily produce the best results, as some of the detailed information was merely noise to the classification algorithm. This is the case for large broadleaved weeds (e.g., S. marianum) or weed patches [8]. The use of lower resolutions and the subsequent speed of processing could lead to effective real-time decision support systems [11].Object-based image analysis (OBIA) is an image processing concept that treats adjacent pixels as objects, taking into consideration parameters of object shape and homogeneity, on top of the spectral information.…”

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confidence: 99%

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

et al. 2018

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Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.hard to control using herbicides in fields, and its thorny leaves and high posture are a nuisance to grazing animals in pastures. It grows in dense strands and is more competitive than grass weeds, growing taller to overshadow adjacent species that compete for light [1].In recent years, unmanned aerial vehicles (UAVs) have proven to be useful in agricultural management. Their applications vary, but usually involve remote sensing, mapping, land modelling and precision agriculture. The most common use of UAVs in weed management is to acquire multispectral images in the visible and infrared spectrum for weed identification and mapping, however, a number of other potential uses have emerged, including the application of pesticides and other agrochemicals by the UAV itself [2]. Mapping weeds using UAVs provides high spatial resolutions, usually on the order of a few centimetres. This is vital in order to detect small weeds, weeds at a young stage, or within low crop cover with high background soil signal. In addition to the advantage of spatial resolution, UAVs offer temporal flexibility and can collect data at any time that fits the user's requirements, as compared to the satellite images. Moreover, UAVs can acquire images during overcast conditions, thus bypassing a frequent problem with satellite remote sensing, although cloud shadows and varying illumination conditions during image acquisition may affect data quality [3,4].A chall...

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“…An eBee fixed wing UAV () with a Canon S110 NIR camera (12 Mpixels) acquired the remote sensing images on 19 May 2015. The spectral bands included are green (560 ± 25 nm), red (625 ± 45 nm) and near-infrared (850 ± 50 nm), the original resolution was 0.1 m, and was rescaled to 0.5 m, as demonstrated by Tamouridou et al (2017) [5]. Beyond the analysis of the spectral information, a texture layer was created based on the NIR layer using the local variance algorithm (moving window size 7 × 7 pixels) depicting the structural patterns of vegetation.…”

Section: Methodsmentioning

confidence: 99%

“…Previous successful applications of UAV in weed mapping include Tamouridou et al (2017) [5] who evaluated the optimum scale for mapping weed patches; Pena 2013 [6] who described weed mapping in early-season maize fields using object-based analysis of UAV images; Torres-Sanchez (2013) [7] who provided configuration and specifications of an UAV for early site specific weed management.…”

Section: Introductionmentioning

confidence: 99%

Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery

Tamouridou

Alexandridis

Pantazi

et al. 2017

Sensors

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Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.

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Evaluation of UAV imagery for mapping Silybum marianum weed patches

Cited by 44 publications

References 23 publications

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery

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