Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

Böhler, Jonas E.; Schaepman, Michael E.; Kneubühler, Mathias

doi:10.3390/rs11151780

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…UAVs or drones are mainly used to capture data with limited spectral resolution, to acquire thermal data, or to produce very highresolution digital elevation models by means of stereophotogrammetry (Coops et al, 2019). UAVs can notably serve to overcome the issue of partially missing spectral resolution with high-density time series (Böhler et al, 2019). Multi-View Stereo analysis (Furukawa and Ponce, 2010) and Structure-from-Motion (Westoby et al, 2012) algorithms are increasingly used as they make it possible to estimate 3D structures from partly overlapping image sequences.…”

Section: Direct Detection and Sampling Of Species And Their Traitsmentioning

confidence: 99%

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Randin

Ashcroft

Bolliger

et al. 2020

Remote Sensing of Environment

179

142

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Section: Direct Detection and Sampling Of Species And Their Traitsmentioning

confidence: 99%

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Randin

Ashcroft

Bolliger

et al. 2020

Remote Sensing of Environment

179

142

View full text Add to dashboard Cite

show abstract

“…In order to reduce the computational load, 1000 pixels were randomly selected for each crop class in the training and validation splits. The remaining two splits were retained for testing the learned model, resulting in a total of 15 permutations (so-called folds) [31].…”

Section: Splitting Of Feature Settingsmentioning

confidence: 99%

Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

2020

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Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of >92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%).

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“…Incorporation of the multispectral indices with the color, texture and shape factors of the agricultural features can significantly improve the detectability of the image analysis methods [38][39][40][41]. Jin et al [42] achieved promising results on the contribution of the textural features and the tillage indices for the detection of CRC and classification of the tillage intensity.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery

2021

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Conservation tillage methods through leaving the crop residue cover (CRC) on the soil surface protect it from water and wind erosions. Hence, the percentage of the CRC on the soil surface is very critical for the evaluation of tillage intensity. The objective of this study was to develop a new methodology based on the semiautomated fuzzy object based image analysis (fuzzy OBIA) and compare its efficiency with two machine learning algorithms which include: support vector machine (SVM) and artificial neural network (ANN) for the evaluation of the previous CRC and tillage intensity. We also considered the spectral images from two remotely sensed platforms of the unmanned aerial vehicle (UAV) and Sentinel-2 satellite, respectively. The results indicated that fuzzy OBIA for multispectral Sentinel-2 image based on Gaussian membership function with overall accuracy and Cohen’s kappa of 0.920 and 0.874, respectively, surpassed machine learning algorithms and represented the useful results for the classification of tillage intensity. The results also indicated that overall accuracy and Cohen’s kappa for the classification of RGB images from the UAV using fuzzy OBIA method were 0.860 and 0.779, respectively. The semiautomated fuzzy OBIA clearly outperformed machine learning approaches in estimating the CRC and the classification of the tillage methods and also it has the potential to substitute or complement field techniques.

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Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

Cited by 10 publications

References 43 publications

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery

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