Automatic palm trees detection from multispectral UAV data using normalized difference vegetation index and circular Hough transform

Mansoori, Saeed Al; Kunhu, Alavi; Al-Ahmad, Hussain

doi:10.1117/12.2325732

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…There exist tree detection methods that use multispectral or RGB cameras and specific descriptors such as crown size, crown contour, foliage cover, foliage color and texture [16]; while others rely on pixel-based classification techniques, such as calculating the Normalized Difference Vegetation Index (NDVI), Circular Hough Transform (CHT) and morphological operators to segment palm trees with an accuracy of 95% [17]. Other methods depend on object-based classification techniques; for example, they use the Random Forest algorithm on multispectral data with an accuracy value of 78% [18], or a naive Bayesian network on high-resolution aerial ortophotos and ancillary data (Digital Elevation Models and forest maps) with an accuracy value of 87% [19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Morales

Kemper

Sevillano

et al. 2018

Forests

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One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the difficult access to these swamps. The contribution of this paper is twofold: the presentation of a dataset called MauFlex, and the proposal of a segmentation and measurement method for areas covered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method performs a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different environment and light conditions using three different RGB cameras. The MauFlex dataset was created from these images and it consists of 25,248 image patches of 512 × 512 pixels and their respective ground truth masks. The results over the test set achieved an accuracy of 98.143%, specificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation.

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Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Morales

Kemper

Sevillano

et al. 2018

Forests

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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

Digital Interaction and Machine Intelligence

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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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“…The performance of their approach was assessed using precision, recall, and F-score, which all scored a value of 98.1% on average. Another example can be seen in [3], where the authors detect and count palm trees in the UAE from Unmanned Aerial Vehicles (UAV) using spectral information and morphological operations. The authors make efficient use of NDVI with histogram equalized Y channel extracted from YCbCr color domain, and then use this information in conjunction with Canny edge detection and measuring the roundness of the detected object to classify it into palm trees or otherwise.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Palm Tree Detection from Remote Sensing Images - UAE Dataset

Al-Saad

Aburaed

Mansoori

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Autonomous detection and counting of palm trees is a research field of interest to various countries around the world, including the UAE. Automating this task saves effort and resources by minimizing human intervention and reducing potential errors in counting. This paper introduces a new High Resolution (HR) remote sensing dataset for autonomous detection of palm trees in the UAE. The dataset is collected using Unmanned Aerial Vehicles (UAV), and it is labeled properly in PASCAL VOC and YOLO formats after pre-processing and visually inspecting its quality. A comparative evaluation between Faster-RCNN and YOLOv4 networks is then conducted to observe the usability of the dataset in addition to the strengths and weaknesses of each network. The dataset is publicly available at https://github.com/NourO93/Palm-Tree-Dataset.

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Automatic palm trees detection from multispectral UAV data using normalized difference vegetation index and circular Hough transform

Cited by 14 publications

References 15 publications

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

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

Autonomous Palm Tree Detection from Remote Sensing Images - UAE Dataset

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