Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

Zhang, Chen; Xia, Kai; Feng, Hailin; Yang, Yinhui; Du, Xiaochen

doi:10.1007/s11676-020-01245-0

Cited by 73 publications

(39 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The capability of VHR images (including 2D and 3D) that the UAV-based systems can offer provides an opportunity and potential for improving mapping individual TS. For example, in mapping ten urban tree species using UAVbased RGB optical images and deep learning methods, Zhang et al [92] achieved an OA of 92.6%. Schiefer et al…”

Section: Lidar Sensors the Most Basic Data Measured Bymentioning

confidence: 99%

“…The ML models may include RF, SVM, and artificial neural network (ANN), which are all nonparametric classifiers and can take multiple input variables/features (e.g., spectral, textural, spatial/geometric, VI, and other ancillary variables) [17,34,38,160]. The convolutional neural network-(CNN-) based methods include different architectures/models of CNNs, such as AlexNet, VGG-16, ResNet-50 [3,92], and 3D-CNN [107,139]. More recently, the CNN models have been widely used in image classification tasks including TS classification (e.g., [3,119,161]).…”

Section: Spectral Mixture Analysesmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

2021

J Remote Sens

View full text Add to dashboard Cite

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.

show abstract

Section: Lidar Sensors the Most Basic Data Measured Bymentioning

confidence: 99%

Section: Spectral Mixture Analysesmentioning

confidence: 99%

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

2021

J Remote Sens

View full text Add to dashboard Cite

show abstract

“…The mapping and detection of individual tree crowns, tree/plant/vegetation species, crops, and wetlands from UAV-based images are achieved by diverse CNN architectures, which are used to perform different tasks, including path-based classification [78][79][80][81][82][83][84][85][86][87], object detection [88][89][90][91][92][93][94][95][96][97], and semantic segmentation [98][99][100][101][102][103][104][105][106][107]. Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108].…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

View full text Add to dashboard Cite

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

show abstract

“…Any one data source could be used for plant species classification, but combining information from multiple sources is valuable, albeit difficult (Torabzadeh, Morsdorf & Schaepman, 2014;Anderson et al, 2008;Asner et al, 2012). Deep neural networks automatically extract intricate patterns and identify trends from large volumes of data (LeCun, Bengio & Hinton, 2015), which makes them useful for classification and data fusion tasks (Zhu et al, 2017;Ma et al, 2019), including plant species classification (Brodrick, Davies & Asner, 2019;Fricker et al, 2019;Zhang et al, 2020;Onishi & Ise, 2021). At a high level, neural networks are flexible function approximators that learn a mapping from inputs (e.g., spectral or lidar data) to outputs (e.g., species classes), by way of a sequence of matrix multiplications and nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data

Scholl

McGlinchy

Price-Broncucia

et al. 2021

PeerJ

View full text Add to dashboard Cite

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that “learns” how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts.

show abstract

Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

Cited by 73 publications

References 29 publications

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data

Contact Info

Product

Resources

About