scholarly journals NEON NIST data science evaluation challenge: methods and results of team FEM

2018 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Data Science ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Region Growing ◽  
Support Vector ◽  
Infrared Band ◽  
Species Classification ◽  
Individual Tree ◽  
Data Alignment ◽  
Selection Of

An international data science challenge, called NEON NIST data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: 1) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the segmentation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

scholarly journals NEON NIST data science evaluation challenge: methods and results of team FEM

2018 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Data Science ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Region Growing ◽  
Support Vector ◽  
Infrared Band ◽  
Species Classification ◽  
Individual Tree ◽  
Data Alignment ◽  
Selection Of

An international data science challenge, called NEON NIST data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: 1) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the segmentation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

scholarly journals Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6227 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Data Science ◽  
Remote Sensing Data ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Sensing Data ◽  
Tree Species Classification ◽  
Selection Of

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

scholarly journals A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

scholarly journals Remote sensing pipeline for tree segmentation and classification in a mixed softwood and hardwood system

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e5837 ◽  
Author(s):  
Conor A. McMahon
Keyword(s):  
Remote Sensing ◽  
Data Science ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Watershed Segmentation ◽  
Stem Height ◽  
Plant Identification ◽  
Species Classification ◽  
Individual Tree ◽  
Height Model

The National Institute of Standards and Technology data science evaluation plant identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. I created a pipeline to perform three remote sensing tasks. First, a marker-controlled watershed segmentation thresholded by vegetation index and height was performed to identify individual tree crowns within the canopy height model. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset’s dimensionality through principle component analysis and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

scholarly journals NEON NIST data science evaluation challenge: methods and results of team Conor

2018 ◽  
Author(s):  
Conor A McMahon
Keyword(s):  
Remote Sensing ◽  
Data Science ◽  
Remote Sensing Data ◽  
Watershed Segmentation ◽  
Stem Height ◽  
Plant Identification ◽  
Species Classification ◽  
Individual Tree ◽  
Crown Area ◽  
Tree Crowns

The NIST DSE Plant Identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. To compete in the competition, I created a pipeline to perform three remote sensing tasks. First, a NDVI- and height-thresholded watershed segmentation was performed to identify individual tree crowns using LIDAR height measurements. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset's dimensionality through PCA and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

scholarly journals NEON NIST data science evaluation challenge: methods and results of team Conor

2018 ◽  
Author(s):  
Conor A McMahon
Keyword(s):  
Remote Sensing ◽  
Data Science ◽  
Remote Sensing Data ◽  
Watershed Segmentation ◽  
Stem Height ◽  
Plant Identification ◽  
Species Classification ◽  
Individual Tree ◽  
Crown Area ◽  
Tree Crowns

The NIST DSE Plant Identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. To compete in the competition, I created a pipeline to perform three remote sensing tasks. First, a NDVI- and height-thresholded watershed segmentation was performed to identify individual tree crowns using LIDAR height measurements. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset's dimensionality through PCA and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

scholarly journals An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification

2019 ◽  
Vol 11 (18) ◽  
pp. 2078 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
John Caspersen ◽  
Trevor Jones
Keyword(s):  
Tree Species ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Structural Diversity ◽  
Species Classification ◽  
Individual Tree ◽  
Spectral Overlap ◽  
Object Based ◽  
Early Fall ◽  
Crown Delineation

Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual tree crown (ITC) delineation, non-forest gap elimination, and object-based classification. The process of species classification started with ITC delineation using the spectral angle segmentation algorithm, followed by object-based random forest classifications. A total of 672 trees was located along three triangular transects for training and validation. For single-season images, the late-spring, mid-summer, and early-fall images achieve the overall accuracies of 0.46, 0.42, and 0.35, respectively. Combining the spectral information of the early-spring, mid-summer, and early-fall images increases the overall accuracy of classification to 0.79. However, further adding the late-fall image to separate deciduous and coniferous trees as an extra step was not successful. Compared to traditional four-band (Blue, Green, Red, Near-Infrared) images, the four additional bands of WorldView images (i.e., Coastal, Yellow, Red Edge, and Near-Infrared2) contribute to the species classification greatly (OA: 0.79 vs. 0.53). This study gains insights into the contribution of the additional spectral bands and multi-seasonal images to distinguishing species with seemingly high degrees of spectral overlap.

Selection of Greenhouse gases Monitoring Instrument channels for CO2 in near infrared band

Optik ◽  
2017 ◽  
Vol 144 ◽  
pp. 597-602
Author(s):  
Chunmin Zhang ◽  
Dongdong Liu ◽  
Piao Rong ◽  
Yanfen Li
Keyword(s):  
Greenhouse Gases ◽  
Near Infrared ◽  
Infrared Band ◽  
Monitoring Instrument ◽  
Selection Of

scholarly journals PERFORMANCE OF THE SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORK CLASSIFIERS FOR ROADS IDENTIFICATION

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 9-14
Author(s):  
A. C. Andrade ◽  
M. J. Alixandrini Jr. ◽  
F. P. S. Carvalho ◽  
V. O. Fernandes
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Laser Scanning ◽  
Near Infrared ◽  
Input Image ◽  
Support Vector ◽  
Surface Model ◽  
Infrared Band ◽  
Artificial Neural ◽  
Non Parametric

Abstract. The objective of this project was to compare two non-parametric classification methods (“Support Vector Machine” – SVM and “Artificial Neural Networks” – ANN) of road regions in high spatial resolution images and associated with data from Airborne Laser Scanning. The study aims to verify what kind of influence the layers of attributes have on the performance from respective classifiers: SVM and RNA. Our method based on tests of this classifiers on 4 bands of airborne images and normalization of the digital surface model (DSM) for showing only information on objects height in relation to ground and not of these in relation to the ground and relief, generating band 5. The samples were used to train chosen non-parametric classifiers (training sets for each different input image/landscape). All classifications had the same set of training samples and the same classification parameters. The optimal parameters for classifications were obtained through the existing library in the Weka mining package: LibSVM and LibMultiLayerPerceptron. Our results demonstrated the existence of a direct relationship between the elevation band of the targets in relation to the terrain (band 05) with the improvement of their performance and lower degree of between bands correlation can also be considered a factor that has a positive influence. As for Neural Networks, the experiment results demonstrate that the presence of the near infrared band (band 04) was decisive for the performance improving of certain combinations in relation to others.

scholarly journals A data science challenge for converting airborne remote sensing data into ecological information

2018 ◽  
Author(s):  
Sergio Marconi ◽  
Sarah J. Graves ◽  
Dihong Gong ◽  
Morteza Shahriari Nia ◽  
Marion Le Bras ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quantitative Methods ◽  
Data Science ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Species Classification ◽  
Ecological Information ◽  
The Difference ◽  
Segmentation Task ◽  
Individual Trees

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: 1) crown segmentation, for identifying the location and size of individual trees; 2) alignment, to match ground truthed trees with remote sensing; and 3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks. The crown segmentation task proved to be the most challenging, with the highest-performing algorithm yielding only 34% overlap between remotely sensed crowns and the ground truthed trees. However, most algorithms performed better on larger trees. For the alignment task, an algorithm based on minimizing the difference, in terms of both position and tree size, between ground truthed and remotely sensed crowns yielded a perfect alignment. In hindsight, this task was over simplified by only including targeted trees instead of all possible remotely sensed crowns. Several algorithms performed well for species classification, with the highest-performing algorithm correctly classifying 92% of individuals and performing well on both common and rare species. Comparisons of results across algorithms provided a number of insights for improving the overall accuracy in extracting ecological information from remote sensing. Our experience suggests that this kind of competition can benefit methods development in ecology and biology more broadly.

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