Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward

Näsi, Roope; Viljanen, Niko; Oliveira, Raquel Alves de; Kaivosoja, Jere; Niemeläinen, Oiva; Hakala, Teemu; Markelin, Lauri; Nezami, Somayeh; Suomalainen, Juha; Honkavaara, Eija

doi:10.5194/isprs-archives-xlii-3-1305-2018

Cited by 11 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improvement, even at the level of a few percent, is extremely important because it equalizes the spectral properties of the objects read from the images and it improves the interpretation of the images. Similar results were achieved in the research of Reference [36]. The authors proposed the radiometric correction of hyperspectral images for the assessment of yield quantity and quality of a grass sward.…”

Section: Discussionsupporting

confidence: 76%

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

et al. 2018

View full text Add to dashboard Cite

Imaging from low altitudes is nowadays commonly used in remote sensing and photogrammetry. More and more often, in addition to acquiring images in the visible range, images in other spectral ranges, e.g., near infrared (NIR), are also recorded. During low-altitude photogrammetric studies, small-format images of large coverage along and across the flight route are acquired that provide information about the imaged objects. The novelty presented in this research is the use of the modified method of the dark-object subtraction technique correction with a modified Walthall’s model for correction of images obtained from a low altitude. The basic versions of these models have often been used to radiometric correction of satellite imagery and classic aerial images. However, with the increasing popularity of imaging from low altitude (in particular in the NIR range), it has also become necessary to perform radiometric correction for this type of images. The radiometric correction of images acquired from low altitudes is important from the point of view of eliminating disturbances which might reduce the capabilities of image interpretation. The radiometric correction of images acquired from low altitudes should take into account the influence of the atmosphere but also the geometry of illumination, which is described by the bidirectional reflectance distribution function (BRDF). This paper presents a method of radiometric correction for unmanned aerial vehicle (UAV) NIR images. The study presents a method of low-altitude image acquisition and a fusion of the method of the dark-object subtraction technique correction with a modified Walthall’s model. The proposed solution performs the radiometric correction of images acquired in the NIR range with the root mean square error (RMSE) value not exceeding 10% with respect to the original images. The obtained results confirm that the proposed method will provide effective compensation of radiometric disturbances in UAV images.

show abstract

Section: Discussionsupporting

confidence: 76%

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Based on our study, we suggest that if the image is radiometrically corrected, the multi-variate linear regression of mean, standard deviation, and four Haralick texture GLCMs of NDVI could be a strong indicator of PPC over NIR brightness, as it provided the highest correlation with field-derived PPC. Nevertheless, radiometric correction of UAS imagery has proven to be a sophisticated process and sometimes difficult for delivering accurate results [41,58,60].…”

Section: Discussionmentioning

confidence: 99%

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Johansen

Phinn

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

show abstract

“…The datasets also give possibilities for new types of analysis, such as utilizing the spectral DSM more rigorously [21,22] and utilizing the multiview spectral datasets in the analysis [78][79][80]. Our future objective will be to develop generalized estimators that can be used without in situ training data, for example, training an estimator with a dataset from one sample area and then using it in other areas.…”

Section: Discussionmentioning

confidence: 99%

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

et al. 2018

View full text Add to dashboard Cite

Abstract:The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy.

show abstract

Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward

Cited by 11 publications

References 18 publications

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Contact Info

Product

Resources

About