scholarly journals Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks

2019 ◽  
Author(s):  
Ben. G. Weinstein ◽  
Sergio Marconi ◽  
Stephanie Bohlman ◽  
Alina Zare ◽  
Ethan White
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Individual Tree ◽  
Detection Model ◽  
High Resolution Imagery ◽  
Observation Network ◽  
Improved Performance ◽  
Crown Delineation ◽  
Individual Trees ◽  
True Color

AbstractRemote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in true color, or red/green blue (RGB) imagery using a deep learning detection network. Individual crown delineation is a persistent challenge in studies of forested ecosystems and has primarily been addressed using three-dimensional LIDAR. We show that deep learning models can leverage existing lidar-based unsupervised delineation approaches to initially train an RGB crown detection model, which is then refined using a small number of hand-annotated RGB images. We validate our proposed approach using an open-canopy site in the National Ecological Observation Network (NEON). Our results show that combining LIDAR and RGB methods in a self-supervised model improves predictions of trees in natural landscapes. The addition of a small number of hand-annotated trees improved performance over the initial self-supervised model. While undercounting of individual trees in complex canopies remains an area of development, deep learning can increase the performance of remotely sensed tree surveys.

scholarly journals Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

2019 ◽  
Vol 11 (11) ◽  
pp. 1309 ◽  
Author(s):  
Ben G. Weinstein ◽  
Sergio Marconi ◽  
Stephanie Bohlman ◽  
Alina Zare ◽  
Ethan White
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Training Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Detection Model ◽  
High Resolution Imagery ◽  
The Neural Network ◽  
Detection Approach ◽  
Crown Delineation

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.

scholarly journals Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2725
Author(s):  
Qixia Man ◽  
Pinliang Dong ◽  
Xinming Yang ◽  
Quanyuan Wu ◽  
Rongqing Han
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Urban Vegetation ◽  
Individual Tree ◽  
Cloud Data ◽  
Random Forest Classification ◽  
The Individual ◽  
Individual Trees

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

scholarly journals INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 581-585
Author(s):  
S. Kuikel ◽  
B. Upadhyay ◽  
D. Aryal ◽  
S. Bista ◽  
B. Awasthi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tree Crown ◽  
Individual Tree ◽  
Banana Tree ◽  
Crown Delineation ◽  
Uav Images

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

scholarly journals Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 295
Author(s):  
Kunyong Yu ◽  
Zhenbang Hao ◽  
Christopher J. Post ◽  
Elena A. Mikhailova ◽  
Lili Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Plantation Forest ◽  
Remote Sensing Images ◽  
Individual Tree ◽  
Tree Detection ◽  
Band Combination ◽  
Individual Trees ◽  
Lm Algorithm ◽  
Multi Band

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees.

scholarly journals DeepForest: A Python package for RGB deep learning tree crown delineation

2020 ◽  
Author(s):  
Ben. G. Weinstein ◽  
Sergio Marconi ◽  
Mélaine Aubry-Kientz ◽  
Gregoire Vincent ◽  
Henry Senyondo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Training Data ◽  
Fine Tuning ◽  
List Type ◽  
Crown Delineation ◽  
Using Data ◽  
Individual Trees ◽  
Tree Crown Delineation ◽  
Python Package

AbstractRemote sensing of forested landscapes can transform the speed, scale, and cost of forest research. The delineation of individual trees in remote sensing images is an essential task in forest analysis. Here we introduce a new Python package, DeepForest, that detects individual trees in high resolution RGB imagery using deep learning.While deep learning has proven highly effective in a range of computer vision tasks, it requires large amounts of training data that are typically difficult to obtain in ecological studies. DeepForest overcomes this limitation by including a model pre-trained on over 30 million algorithmically generated crowns from 22 forests and fine-tuned using 10,000 hand-labeled crowns from 6 forests.The package supports the application of this general model to new data, fine tuning the model to new datasets with user labeled crowns, training new models, and evaluating model predictions. This simplifies the process of using and retraining deep learning models for a range of forests, sensors, and spatial resolutions.We illustrate the workflow of DeepForest using data from the National Ecological Observatory Network, a tropical forest in French Guiana, and street trees from Portland, Oregon.

scholarly journals Semi-Supervised Deep Learning for Lunar Crater Detection Using CE-2 DOM

2021 ◽  
Vol 13 (14) ◽  
pp. 2819
Author(s):  
Sudong Zang ◽  
Lingli Mu ◽  
Lina Xian ◽  
Wei Zhang
Keyword(s):  
Deep Learning ◽  
Landing Site ◽  
Training Data ◽  
The Moon ◽  
Detection Model ◽  
Processing Times ◽  
Crater Detection ◽  
High Resolution Imagery ◽  
Model Training ◽  
Digital Orthophoto

Lunar craters are very important for estimating the geological age of the Moon, studying the evolution of the Moon, and for landing site selection. Due to a lack of labeled samples, processing times due to high-resolution imagery, the small number of suitable detection models, and the influence of solar illumination, Crater Detection Algorithms (CDAs) based on Digital Orthophoto Maps (DOMs) have not yet been well-developed. In this paper, a large number of training data are labeled manually in the Highland and Maria regions, using the Chang’E-2 (CE-2) DOM; however, the labeled data cannot cover all kinds of crater types. To solve the problem of small crater detection, a new crater detection model (Crater R-CNN) is proposed, which can effectively extract the spatial and semantic information of craters from DOM data. As incomplete labeled samples are not conducive for model training, the Two-Teachers Self-training with Noise (TTSN) method is used to train the Crater R-CNN model, thus constructing a new model—called Crater R-CNN with TTSN—which can achieve state-of-the-art performance. To evaluate the accuracy of the model, three other detection models (Mask R-CNN, no-Mask R-CNN, and Crater R-CNN) based on semi-supervised deep learning were used to detect craters in the Highland and Maria regions. The results indicate that Crater R-CNN with TTSN achieved the highest precision (of 91.4% and 88.5%, respectively) in the Highland and Maria regions, even obtaining the highest recall and F1 score. Compared with Mask R-CNN, no-Mask R-CNN, and Crater R-CNN, Crater R-CNN with TTSN had strong robustness and better generalization ability for crater detection within 1 km in different terrains, making it possible to detect small craters with high accuracy when using DOM data.

scholarly journals Hybrid Techniques for Object Detection using Deep Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 1959-1963
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Lane Detection ◽  
Single Shot ◽  
Efficient Utilization ◽  
The Road ◽  
Detection Model ◽  
Hybrid Techniques ◽  
Geographical Regions ◽  
Improved Performance

Deep learning is a one of the major concept of Artificial Intelligence and Machine learning, which deals with the object detection task. On the other hand, a new targeted dataset is built according to commonly used existing datasets, and two networks called Single Shot Multi box Detector (SSD) and You Only Look Once (YOLO) are chosen to work on this new dataset. Through experimentation strengthen the understanding of these networks, and through the analysis of the results, learn the importance of targeted and inclusive datasets for deep learning. In addition, to this optimize the networks for efficient utilization when integrated with the necessary system or application. Further, explore the applications corresponding to these networks. The implementation includes two major concepts. The first concept is Object detection. Object detection is the process of object recognition and classification. There are several Training sets available online for training an object detection model. But the models are not trained to detect the same object from different geographical regions. The second concept is lane detection and steering suggestion. The model detects using the concept of radius or curvature of the road and also distance of the car from both the lane lines. Using these parameters it also gives steering suggestions such as move right or left by a certain distance. In addition to this it gives the distance and speed attributes of the surrounding objects such as cars, motorcycles, etc. Finally, the model developed is capable of detecting all the parameters required in order to be integrated and to create a self-driving car and it can be used efficiently in India. Using the above parameters that are obtained from the model the car can navigate through lanes in real-time. Its improved performance is due to the fact that it can detect road specific objects and because it is specifically trained for Indian roads.

scholarly journals INDIVIDUAL TREE CROWN DELINEATION FROM HIGH SPATIAL RESOLUTION IMAGERY USING U-NET

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 61-66
Author(s):  
B. Hu ◽  
W. Jung
Keyword(s):  
Deep Learning ◽  
Mixed Forest ◽  
Learning Networks ◽  
Tree Crown ◽  
Individual Tree ◽  
Good Opportunity ◽  
Tree Crowns ◽  
Forest Region ◽  
Crown Delineation ◽  
Tree Crown Delineation

Abstract. The objective of this study was to explore the utilization of deep learning networks in individual tree crown (ITC) delineation, a very important step in individual tree analysis. Even though many traditional machine learning methods have been developed for ITC delineation, the accuracy remains low, especially for dense forests where branches, crowns, and clusters of trees usually have similar characteristics and boundaries of tree crowns are not distinct. Advance in deep learning provides a good opportunity to improve ITC delineation. In this study, U-net, Residual U-net, and attention U-net were implemented for the first time in ITC delineation. In order to ensure that the boundaries of tree crowns were classified correctly, a weight map was generated to give more weights to boundary pixels between two close crowns in the loss function. These three networks were trained and tested using optical imagery obtained over a study site within the Great Lakes-St. Lawrence forest region, Ontario Canada. Based on two test sites dominated by open mixed forest and closed deciduous forests, respectively, the overall accuracies were 0.94 and 0.90, respectively for U-net, 0.89 and 0.62 for Residual U-net, and 0.96 and 0.83 for attention U-net.

Density-Based Individual Tree Detection from Three-Dimensional Point Clouds

2021 ◽  
Author(s):  
Carlo Camporeale ◽  
Melissa Latella ◽  
Fabio Sola
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Point Clouds ◽  
Individual Tree ◽  
Tree Detection ◽  
Round Shape ◽  
Point Density ◽  
Individual Trees

<p>The use of three-dimensional point clouds in forestry is steadily increasing. Numerous algorithms to detect individual trees from point clouds and derive some fundamental inventory parameters have been proposed so far, but they usually provide higher accuracy in coniferous stands than in deciduous one. In the latter kind of stands, indeed, the tree identification is hampered by the geometrical round shape of the crowns, the interlacing branches of adjacent trees and the usual presence of understory vegetation.</p><p>In an attempt to overcome these limitations, we developed an algorithm that is innovatively based on the areal point density of the three-dimensional cloud and that provides the height and coordinates of all the trees within a region of interest.</p><p>In this work, we apply the algorithm to different situations, ranging from the regularly-arranged plantations to the very interlaced crowns of the naturally established stands, demonstrating how it is able to correctly detect most of the trees and recreate a map of their spatial distribution. We also test its capability to deal with relatively low point density and explore the possibility to use it to recreate time series of vegetation biomass. Finally, we discuss the algorithm’s limitations and potentialities, particularly focusing on its coupling to other existing tools to deal with a wider range of applications in forestry and land management.</p><p> </p>

scholarly journals Computational Prediction of Disease Detection and Insect Identification using Xception model

2021 ◽  
Author(s):  
Lidia Cleetus ◽  
Raji Sukumar ◽  
Hemalatha N
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Computational Prediction ◽  
Detection Model ◽  
Insect Identification ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Improved Performance ◽  
Learning Architectures ◽  
Base Data

In this paper, a detection tool has been built for the detection and identification of the diseases and pests found in the crops at its earliest stage. For this, various deep learning architectures were experimented to see which one of those would help in building a more accurate and an efficient detection model. The deep learning architectures used in this study were Convolutional Neural Network, VGG16, InceptionV3, and Xception. VGG16, InceptionV3, and Xception are categorized as the pre-trained models based on CNN architecture. They follow the concept of transfer learning. Transfer learning is a technique which makes use of the learnings of the models previously trained on a base data and applies it to the present dataset. This is an efficient technique which gives us rapid results and improved performance. Two plant datasets have been used here for disease and insects. The results of the algorithms were then compared. Most successful one has been the Xception model which obtained 82.89 for disease and 77.9 for pests.

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