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

Jurado, Juan M.; Ortega, Lidia; Cubillas, Juan José; Feito, Francisco R.

doi:10.3390/rs12071106

Cited by 61 publications

(42 citation statements)

References 59 publications

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“…There are several methods for the automatic detection and parameters extraction on 3D models using crop height models [37], combining terrestrial laser scanner and UAV photogrammetric point clouds [38], fusing RGB and multispectral point clouds to extract individual tree parameters [39], computing 3D vegetation indices in olive groves [40], and obtaining forest structural attributes [41]. However, given the complexity and the unique characteristics of vineyard plots, such methods are not suitable to be applied.…”

Section: Individual Grapevine Detectionmentioning

confidence: 99%

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

et al. 2020

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The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

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Section: Individual Grapevine Detectionmentioning

confidence: 99%

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

et al. 2020

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“…Alternative to multispectral LIDAR approach, in the study by Jurado et al [35], a more cost-effective method, photogrammetry, is used to construct 3D images of olive trees. A high resolution camera is mounted on an unmanned aerial vehicle (UAV) to take multispectral images which are then reconstructed into 3D images.…”

Section: Environment Monitoringmentioning

confidence: 99%

Spectral imaging and spectral LIDAR systems: moving toward compact nanophotonics-based sensing

Wang

et al. 2021

Nanophotonics

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With the emerging trend of big data and internet-of-things, sensors with compact size, low cost and robust performance are highly desirable. Spectral imaging and spectral LIDAR systems enable measurement of spectral and 3D information of the ambient environment. These systems have been widely applied in different areas including environmental monitoring, autonomous driving, biomedical imaging, biometric identification, archaeology and art conservation. In this review, modern applications of state-of-the-art spectral imaging and spectral LIDAR systems in the past decade have been summarized and presented. Furthermore, the progress in the development of compact spectral imaging and LIDAR sensing systems has also been reviewed. These systems are based on the nanophotonics technology. The most updated research works on subwavelength scale nanostructure-based functional devices for spectral imaging and optical frequency comb-based LIDAR sensing works have been reviewed. These compact systems will drive the translation of spectral imaging and LIDAR sensing from table-top toward portable solutions for consumer electronics applications. In addition, the future perspectives on nanophotonics-based spectral imaging and LIDAR sensing are also presented.

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“…In this section, we describe the process to enrich the RGB point cloud using the spectral reflectance in order to get a more detailed knowledge of the behavior of all existing materials. This approach is based on our previous work presented by Jurado et al [29]. Consequently, the comprehensive 3D model can also contain multispectral features by mapping the reflectance and NDVI maps on the geometry.…”

Section: Heterogeneous Data Fusionmentioning

confidence: 99%

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Jurado

Cárdenas

Ogáyar

et al. 2020

Sensors

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The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

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

Cited by 61 publications

References 59 publications

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

Spectral imaging and spectral LIDAR systems: moving toward compact nanophotonics-based sensing

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

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