Palm trees detecting and counting from high-resolution WorldView-3 satellite images in United Arab Emirates

AlMaazmi, Alya

doi:10.1117/12.2325733

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…3D): yield estimation (51.58%), phenotyping (27.60%), livestock monitoring (10.85%), insect monitoring (6.78%) and others (3.19%). The category 'others' includes applications related to the field management of orchards, such as pruning [43,44], path planning [45], plant water status [46], monitoring fruit ripeness [47] and monitoring resources [48,49].…”

Section: Literature Review Resultsmentioning

confidence: 99%

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Farjon¹,

Liu²,

Edan³

2023

Preprint

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The number of objects is considered an important factor in a variety of tasks in the agricultural domain. Automated counting can improve farmers' decisions regarding yield estimation, stress detection, disease prevention, and more. In recent years, deep learning has been increasingly applied to many agriculture-related applications, complementing conventional computer-vision algorithms for counting agricultural objects. This article reviews progress in the past decade and the state of the art for counting methods in agriculture, focusing on deep-learning methods. It presents an overview of counting algorithms, metrics, platforms, and sensors, a list of all publicly available datasets, and an in-depth discussion of various deep-learning methods used for counting. Finally, it discusses open challenges in object counting using deep learning and gives a glimpse into new directions and future perspectives for counting research. The review reveals a major leap forward in object counting in agriculture in the past decade, led by the penetration of deep learning methods into counting platforms.

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Section: Literature Review Resultsmentioning

confidence: 99%

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Farjon¹,

Liu²,

Edan³

2023

Preprint

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“…Few attempts has been published to detect different objects in drone footage using CNNs based learning as in [27][28][29][30] and [31], etc. The binary classifiers for the detection of palm trees, as one of the objects in the designed multiclass detector, can be found in [32][33][34][35][36][37] and [37,38] and [39]. For more details we refer the readers to [5].…”

Section: Related Workmentioning

confidence: 99%

A Coarse-to-Fine Multi-class Object Detection in Drone Images Using Convolutional Neural Networks

Aburasain

Edirisinghe

Zamim

2022

Lecture Notes in Networks and Systems

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Multi-class object detection has a rapid evolution in the last few years with the rise of deep Convolutional Neural Networks (CNNs) learning based, in particular. However, the success approaches are based on high resolution ground level images and extremely large volume of data as in COCO and VOC datasets. On the other hand, the availability of the drones has been increased in the last few years and hence several new applications have been established. One of such is understanding drone footage by analysing, detecting, recognizing different objects in the covered area. In this study conducted, a collection of large images captured by a drone flying at a fixed altitude in a desert area located within the United Arab Emirates (UAE) is given and it is utilised for training and evaluating the CNN networks to be investigated. Three state-of-the-art CNN architectures, namely SSD-500 with VGGNet-16 meta-architecture, SSD-500 with ResNet meta-architecture and YOLO-V3 with Darknet-53 are optimally configured, re-trained, tested and evaluated for the detection of three different classes of objects in the captured footage, namely, palm trees, group-of-animals/cattle and animal sheds in farms. Our preliminary experiments revealed that YOLO-V3 outperformed SSD-500 with VGGNet-16 by a large margin and has a considerable improvement as compared to using SSD-500 with ResNet. Therefore, it has been selected for further investigation, aiming to propose an efficient coarse-to-fine object detection model for multi-class object detection in drone images. To this end, the impact of changing the activation function of the hidden units and the pooling type in the pooling layer has been investigated in detail. In addition, the impact of tuning the learning rate and the selection of the most effective optimization method for general hyper-parameters tuning is also investigated. The result demonstrated that the multi-class object detector developed has precision of 0.99, a recall of 0.94 and an F-score of 0.96, proving the efficiency of the multi-class object detection network developed.

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“…Reference [23] also showed the efficient performance of WV-2 and WV-3 to classify six commercial forest species in South Africa, obtaining an overall accuracy of 85%. AlMaazmi et al [24] classified palm trees in the United Arab Emirates (UAE) with WV-3, with a satisfactory overall accuracy of 89%.…”

Section: Introductionmentioning

confidence: 99%

Mapping Chestnut Stands Using Bi-Temporal VHR Data

et al. 2019

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This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife.

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Palm trees detecting and counting from high-resolution WorldView-3 satellite images in United Arab Emirates

Cited by 6 publications

References 8 publications

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

A Coarse-to-Fine Multi-class Object Detection in Drone Images Using Convolutional Neural Networks

Mapping Chestnut Stands Using Bi-Temporal VHR Data

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