Resnet-Based Tree Species Classification Using Uav Images

Natesan, Sowmya; Armenakis, Costas; Vepakomma, Udayalakshmi

doi:10.5194/isprs-archives-xlii-2-w13-475-2019

Cited by 71 publications

(53 citation statements)

References 21 publications

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“…Considering UAV platforms, Natesan et al [29] proposed a deep learning framework for tree species classification. In this approach, images of pre-delineated tree crowns were the inputs to a CNN to classify the delineated trees to one out of three classes: red pine, white pine, and non-pine.…”

Section: Introductionmentioning

confidence: 99%

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Torres

Feitosa

Happ

et al. 2020

Sensors

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This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.

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Section: Introductionmentioning

confidence: 99%

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Torres

Feitosa

Happ

et al. 2020

Sensors

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“…The achieved accuracy is 95.77% and 97.32% by beating the best result. S. Natesan et al [11] constructs a tree classifier using UAV Images using and ResNet. High-resolution RGB images are gathered from a camera mounted on a UAV platform over 3 years that varied in numerous acquisition parameters such as season, time, illumination and angle to train.…”

Section: Literature Surveymentioning

confidence: 99%

Common Bamboo Species Identification using Machine Learning and Deep Learning Algorithms

Juyal¹,

Kulshrestha²,

Sharma³

et al. 2020

IJITEE

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Due to its growth rate and strength, bamboo's versatility is huge. Bamboo has been developed to replace hardwood naturally. But it can be difficult to recognize a bamboo as many appear in a cluster or singular. Each bamboo type has its applications. Because of the utility of bamboo, we have worked in Random Forest, naive bays, logistic regression, the SVM-kernel, CNN, and ResNET, amongst several machine-learning algorithms. A similar test was carried out and delineated using graphs based on uncertainty matrix parameters and training accuracy. In this paper, we have used the data of following five species such as Phyllostachys nigra, Bambusa vulgaris ‘Striata‘, Dendrocalamus giganteu, Bambusa ventricosa, and Bambusa tulda which are generally found in north India. We trained, tested and validated the species from datasets using different machine learning and deep learning algorithms.

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“…Wetlands are protected environments with limited ground accessibility making UAVs particularly appropriate for data collection. UAVs offer the possibility to cover large areas with high resolution images, and they have proved their usefulness in a variety of studies in agriculture [ 16 , 17 ] and forestry [ 18 , 19 , 20 ]. Still, UAV images present challenges like the pre-pocessing of the data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning

Kentsch

Cabezas

Tomhave

et al. 2021

Sensors

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Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques.

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Resnet-Based Tree Species Classification Using Uav Images

Cited by 71 publications

References 21 publications

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Common Bamboo Species Identification using Machine Learning and Deep Learning Algorithms

Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning

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