Improved vegetation segmentation with ground shadow removal using an HDR camera

Suh, Hyun Kwon; Hofstee, J.W.; Henten, E.J. van

doi:10.1007/s11119-017-9511-z

Cited by 39 publications

(25 citation statements)

References 49 publications

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“…The images were resized to 1296×966 when working with Matlab R2017a. The evaluation Random Forest classifier is done quantitatively using the Confusion Matrix [34] . The confusion matrix gives a summary of the prediction done on a classification problem.…”

Section: Resultsmentioning

confidence: 99%

Crop and weed discrimination using Laws’ texture masks

Kamath

Balachandra

Prabhu

2020

International Journal of Agricultural and Biological Engineering

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Computers have become an integral part of human lives. Computers are used in almost every field even in agriculture. Technologies like computer vision-based pattern recognition are being used to detect diseases and pests like weeds affecting the crop. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight. It can significantly reduce the quality and yield of the crops incurring a huge loss to the farmers. This paper investigates the use of texture features extracted from Laws' texture masks for discrimination of Carrot crops and weeds in digital images. Laws' texture method is one of the popular methods used to extract texture features in medical image processing, though not much explored in plant-based images or agricultural images. This experiment was carried out on two categories of benchmark digital image datasets of Carrot crop and Carrot weed respectively, which are publicly available. A total of 70 texture features were extracted. The dimensionality reduction technique was used to get the optimal features. These features were then used to train the Random Forest classifier. The results and observations from the experiment showed that the classifier achieved above 94% accuracy.

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Section: Resultsmentioning

confidence: 99%

Crop and weed discrimination using Laws’ texture masks

Kamath

Balachandra

Prabhu

2020

International Journal of Agricultural and Biological Engineering

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“…Thus, vegetation monitoring is possible through feature extraction to promote sustainable farming [29]. Other approaches used multispectral data to propose a 2D-based analysis for disease detection [30] and segmentation of vegetation areas [31]. In addition, recent contributions are provided by the fusion of LiDAR and hyperspectral remotely sensed data [32].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

et al. 2020

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3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.

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“…Bargoti and Underwood (2017) proposed a pixel-wise approach for image segmentation using convolutional neural networks with multi-scale and multi-layered perceptron to respond to the change in illumination conditions. Suh, Hofstee and van Henten (2018) developed an algorithm based on a multi-level threshold and color space conversion for detecting and removing vegetation shadows on the ground. By analyzing the above methods, there are many the following shortcomings in the existing shadow detection algorithm:…”

Section: ． Introductionmentioning

confidence: 99%

Orthophoto Shadow Detection Method Under Artificial Shadow

Zhang

Zhou

Yue

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Shadows are ubiquitous in high-resolution images, especially in urban regions where there are more serious shadow occlusions. In order to improve the detection effect of shadows, this paper analyzes the characteristics and properties of shadows in orthophotos, and proposes an orthophoto shadow detection method under artificial shadow. Firstly, the shadow modeling tool is used to calculate the shadow regions (i.e. artificial shadow) caused by the building obstructing the sun's rays. Secondly, the relaxation matching algorithm is extended by the position and the shape of the shadow polygon as characteristic constraints. The relaxation matching algorithm is extended by the position and shape as shadow polygon’s characteristic constraints. Thirdly, the feature constraint value is calculated which between the shadow polygons of the two images. The correlation coefficient is used to obtain the initial probability value of each shadow polygon in the orthophoto. Finally, the optimal solution is obtained by continuous correction and iteration of the initial probability value. The method performs an overall matching of the two images and obtains the position of the shadow regions of the buildings in the orthophoto image. Experiment shows that this method reduces the mismatch rate and improves the matching accuracy. This method can detect shadow regions of buildings in orthophoto quickly and efficiently.

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Improved vegetation segmentation with ground shadow removal using an HDR camera

Cited by 39 publications

References 49 publications

Crop and weed discrimination using Laws’ texture masks

Crop and weed discrimination using Laws’ texture masks

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

Orthophoto Shadow Detection Method Under Artificial Shadow

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