scholarly journals Fusion of Unmanned Aerial Vehicle Panchromatic and Hyperspectral Images Combining Joint Skewness-Kurtosis Figures and a Non-Subsampled Contourlet Transform

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3467 ◽  
Author(s):  
Jinling Zhao ◽  
Chengquan Zhou ◽  
Linsheng Huang ◽  
Xiaodong Yang ◽  
Bo Xu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Contourlet Transform ◽  
Area Index ◽  
Spatial Entropy ◽  
Colour Image ◽  
Central Pixel ◽  
Fused Image ◽  
Aerial Vehicle ◽  
False Colour ◽  
Stage Stage

To obtain fine and potential features, a highly informative fused image created by merging multiple images is usually required. In our study, a novel fusion algorithm called JSKF-NSCT is proposed for fusing panchromatic (PAN) and hyperspectral (HS) images by combining the joint skewness-kurtosis figure (JSKF) and the non-subsampled contourlet transform (NSCT). The JSKF model is used first to derive the three most sensitive bands from the original HS image according to the product of the skewness and the kurtosis coefficients of each band. Afterwards, an intensity-hue-saturation (IHS) transform is used to obtain the luminance component I of the produced false-colour image consisting of the above three bands. Then the NSCT method is used to decompose component I of the false-colour image and the PAN image. The weight-selection rule based on the regional energy is adopted to acquire the low-frequency coefficients and the correlation between the central pixel and its surrounding pixels is used to select the high-frequency coefficients. Finally, the fused image is obtained by applying an IHS inverse transform and an inverse NSCT transform. The unmanned aerial vehicle (UAV) HS and PAN images under low- and high-vegetation coverage of wheat at the flag leaf stage (Stage I) and the grain filling stage (Stage II) are used as the sample data sources. The fusion results are comparatively validated using spatial (entropy, standard deviation, average gradient and mean) and spectral (normalised difference vegetation, NDVI, and leaf area index, LAI) assessments. Additional comparative studies using anomaly detection and pixel clustering also demonstrate that the proposed method outperforms other methods. They show that the algorithm reported herein can better preserve the original spatial and spectral characteristics of the two types of images to be fused and is more stable than IHS, principal components analysis (PCA), non-negative matrix factorization (NMF) and Gram-Schmidt (GS).

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle

2013 ◽  
Vol 115 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Juan I. Córcoles ◽  
Jose F. Ortega ◽  
David Hernández ◽  
Miguel A. Moreno
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Allium Cepa ◽  
Area Index ◽  
Allium Cepa L ◽  
Aerial Vehicle

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals Estimation of Canopy Area of Fruit Trees Using Light Unmanned Aerial Vehicle (UAV) and Image Processing Methods

2020 ◽  
Vol 8 (5) ◽  
pp. 1039-1042
Author(s):  
Adil Koray Yıldız ◽  
Hakan Keles ◽  
Servet Aras
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicle ◽  
Fruit Trees ◽  
Processing Methods ◽  
Area Index ◽  
Canopy Development ◽  
Canopy Area ◽  
The Status ◽  
Aerial Vehicle ◽  
Method R

Some vegetative properties measured in fruit trees are important indicators in examining of plant growth calculation, estimation of leaf area index in evapotranspiration, fertilizer requirement etc. These measurements reflect the effects of the cultivation treatments in many areas of commercial growing and scientific studies. One of the most important measurements is the status of the canopy development. Canopy width, area and volume can be measured with some calculations. However, more technological equipment may be needed to reduce work and labor, and to make the results more precise and clearer. Recently, unmanned aerial vehicles, which have become widespread, have a wide potential for use in agriculture. By using image processing methods, it is possible to make more objective and high accuracy evaluations much faster. In this study, the images of the apple trees (Malus domestica Borkh) cultivar Golden grafted onto MM106 rootstock, were taken by light unmanned aerial vehicle to calculate the canopy area and then these images were analyzed using image processing methods for calculating canopy areas. Both circular and elliptical calculation methods were used. The area calculations with image processing methods were compared with the areas obtained manually. Comparisons were made by regression analysis. For the most successful method R value was 0.9662 for elliptic area and 0.9346 for circular area which was calculated by image processing. The results demonstrated that the image processing can be an alternative method to determine the canopy area according to accuracy ratios.

scholarly journals Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

2021 ◽  
Vol 13 (6) ◽  
pp. 1187
Author(s):  
Rubén Rufo ◽  
Jose Miguel Soriano ◽  
Dolors Villegas ◽  
Conxita Royo ◽  
Joaquim Bellvert
Keyword(s):  
Bread Wheat ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Agronomic Traits ◽  
Stepwise Multiple Regression ◽  
Area Index ◽  
Rainfed Conditions ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping ◽  
Rgb Images

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.

scholarly journals Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

2020 ◽  
Vol 13 (1) ◽  
pp. 84
Author(s):  
Tomoaki Yamaguchi ◽  
Yukie Tanaka ◽  
Yuto Imachi ◽  
Megumi Yamashita ◽  
Keisuke Katsura
Keyword(s):  
Deep Learning ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Regression Models ◽  
Estimation Model ◽  
Area Index ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Multispectral Camera

Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

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