scholarly journals Species Classification in a Tropical Alpine Ecosystem Using UAV-Borne RGB and Hyperspectral Imagery

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Carol X. Garzon-Lopez ◽  
Eloisa Lasso
Keyword(s):  
Hyperspectral Imagery ◽  
Vascular Plant ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Ground Vegetation ◽  
Anthropogenic Pressure ◽  
Forward Selection ◽  
Species Classification ◽  
The Face ◽  
Sequential Floating Forward Selection

Páramos host more than 3500 vascular plant species and are crucial water providers for millions of people in the northern Andes. Monitoring species distribution at large scales is an urgent conservation priority in the face of ongoing climatic changes and increasing anthropogenic pressure on this ecosystem. For the first time in this ecosystem, we explored the potential of unoccupied aerial vehicles (UAV)-borne red, green, and blue wavelengths (RGB) and hyperspectral imagery for páramo species classification by collecting both types of images in a 10-ha area, and ground vegetation cover data from 10 plots within this area. Five plots were used for calibration and the other five for validation. With the hyperspectral data, we tested our capacity to detect five representative páramo species with different growth forms using support vector machine (SVM) and random forest (RF) classifiers in combination with three feature selection methods and two class groups. Using RGB images, we could classify 21 species with an accuracy greater than 97%. From hyperspectral imaging, the highest accuracy (89%) was found using models built with RF or SVM classifiers combined with a binary grouping method and the sequential floating forward selection feature. Our results demonstrate that páramo species can be accurately mapped using both RGB and hyperspectral imagery.

scholarly journals Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

2018 ◽  
Vol 10 (8) ◽  
pp. 1208 ◽  
Author(s):  
Javier Marcello ◽  
Francisco Eugenio ◽  
Javier Martín ◽  
Ferran Marqués
Keyword(s):  
High Resolution ◽  
Aquatic Vegetation ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Shallow Waters ◽  
Benthic Habitats ◽  
Spectral Angle Mapper ◽  
Seabed Mapping

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

scholarly journals Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

2019 ◽  
Vol 11 (11) ◽  
pp. 1338 ◽  
Author(s):  
Camile Sothe ◽  
Michele Dalponte ◽  
Cláudia Maria de Almeida ◽  
Marcos Benedito Schimalski ◽  
Carla Luciane Lima ◽  
...  
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Species Recognition ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Kappa Index ◽  
Species Classification ◽  
Tree Species Classification

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset.

Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique

2013 ◽  
pp. 43-56
Author(s):  
Ahmed Kharrat ◽  
Karim Gasmi ◽  
Mohamed Ben Messaoud ◽  
Nacéra Benamrane ◽  
Mohamed Abid
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Research Work ◽  
Reduction Rate ◽  
Support Vector ◽  
Forward Selection ◽  
Classification Rate ◽  
Brain Images ◽  
Mr Brain Images ◽  
Sequential Floating Forward Selection

A new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images is proposed. The proposed method uses Wavelets Transform (WT) as input module to Genetic Algorithm (GA) and Support Vector Machine (SVM). It segregates MR brain images into normal and abnormal. This contribution employs genetic algorithm for feature selection which requires much lighter computational burden in comparison with Sequential Floating Backward Selection (SFBS) and Sequential Floating Forward Selection (SFFS) methods. A percentage reduction rate of 88.63% is achieved. An excellent classification rate of 100% could be achieved using the support vector machine. The observed results are significantly better than the results reported in a previous research work employing Wavelet Transform and Support Vector Machine.

Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors

2020 ◽  
Vol 34 (13) ◽  
pp. 2059043 ◽  
Author(s):  
Amandeep Sharma ◽  
Lini Mathew ◽  
Shantanu Chatterji ◽  
Deepam Goyal
Keyword(s):  
Fault Diagnosis ◽  
Intelligent System ◽  
Principal Component ◽  
Maintenance Cost ◽  
Support Vector ◽  
Forward Selection ◽  
Statistical Parameters ◽  
Feature Selection Technique ◽  
Artificial Neural Network Ann ◽  
Sequential Floating Forward Selection

In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor.

scholarly journals File Fragment Recognition Based on Content and Statistical Features

2020 ◽  
Author(s):  
Marzieh Masoumi ◽  
Ahmad Keshavarz
Keyword(s):  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Feature Reduction ◽  
Support Vector ◽  
Selection Algorithm ◽  
K Nearest Neighbor ◽  
Forward Selection ◽  
Sequential Floating Forward Selection ◽  
Comparison Of The Results ◽  
Sequential Forward Selection

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

scholarly journals Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5293 ◽  
Author(s):  
Chen Sun ◽  
Luwei Feng ◽  
Zhou Zhang ◽  
Yuchi Ma ◽  
Trevor Crosby ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tuber Yield ◽  
Hyperspectral Imagery ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Research Station ◽  
Adaptive Boosting ◽  
Tuber Set

Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R2 = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R2 = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.

scholarly journals File Fragment Recognition Based on Content and Statistical Features

2020 ◽  
Author(s):  
Marzieh Masoumi ◽  
Ahmad Keshavarz
Keyword(s):  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Feature Reduction ◽  
Support Vector ◽  
Selection Algorithm ◽  
K Nearest Neighbor ◽  
Forward Selection ◽  
Sequential Floating Forward Selection ◽  
Comparison Of The Results ◽  
Sequential Forward Selection

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

scholarly journals A Novel Performance Metric for Multiclass Subject Invariant Brain Computer Interfaces with Imbalanced Classes

2020 ◽  
Author(s):  
Jesse Sherwood ◽  
Jesse Lowe ◽  
Reza Derakhshani
Keyword(s):  
Search Algorithm ◽  
Confusion Matrix ◽  
Support Vector ◽  
Q Factor ◽  
Forward Selection ◽  
Feature Sets ◽  
Feature Search ◽  
Performance Metric ◽  
Imbalanced Classes ◽  
Sequential Floating Forward Selection

Abstract[Finding suitable common feature sets for use in multiclass subject independent brain-computer interface (BCI) classifiers is problematic due to characteristically large inter-subject variation of electroencephalographic signatures. We propose a wrapper search method using a one versus the rest discrete output classifier. Obtaining and evaluating the quality of feature sets requires the development of appropriate classifier metrics. A one versus the rest classifier must be evaluated by a scalar performance metric that provides feedback for the feature search algorithm. However, the one versus the rest discrete classifier is prone to settling into degenerate states for difficult discrimination problems. The chance of occurrence of degeneracy increases with the number of classes, number of subjects and imbalance between the number of samples in the majority and minority classes. This paper proposes a scalar Quality (Q)-factor to compensate for classifier degeneracy and to improve the convergence of the wrapper search. The Q-factor, calculated from the ratio of sensitivity to specificity of the confusion matrix, is applied as a penalty to the accuracy (1-error rate). This method is successfully applied to a multiclass subject independent BCI using 10 untrained subjects performing 4 motor tasks in conjunction with the Sequential Floating Forward Selection feature search algorithm and Support Vector Machine classifiers.]

Impacts of anthropogenic pressure on the degradation of the forest of Doui Thabet (Saida, Western Algeria) in the context of the restoration

2020 ◽  
Vol 7 (2) ◽  
pp. 88-97
Author(s):  
Aouadj Sid Ahmed ◽  
Nasrallah Yahia ◽  
Hasnaoui Okkacha ◽  
Khatir Hadj
Keyword(s):  
Limiting Factor ◽  
Global Changes ◽  
Anthropogenic Pressure ◽  
Qualitative And Quantitative ◽  
Pressure Index ◽  
High Plateau ◽  
The Face ◽  
Western Algeria ◽  
Restoration Strategies ◽  
Very High

AbstractThe forest of Doui Thabet is one of the forests of the Mounts of Saida (Western Algeria) which is experiencing a dynamic regressive. Located in the semi-arid bioclimatic stage, it is located at the edge of two phytogeographic sub-sectors: atlas Tellien Oranais (O3) and high plateau subsector (H1). Among the factors that threaten to curb this fragile and weakened ecosystem, in addition to drought and climate aridity and which has become a structural ecological phenomenon; the overgrazing is also a major limiting factor. This current study provides a qualitative and quantitative assessment of anthropogenic pressure exerted in this area zone. The methodology adopted in this study is that of Le Houerou (1969) and Montoya (1983), which it is based on the calculation of the annual needs of the herd in forage units, the estimate of the feed potential of production, the coefficient of overgrazing and in addition to the anthropogenic pressure index. The result of the forage balance in the forest rangelands of the studied area has a forage deficit (overload) of (96.64%) (a sylvopastoral imbalance), in addition to that, the coefficient of overgrazing is (92.3%) and the anthropogenic pressure index is very high (28). The conservation and the restoration of this area is a major concern in the face of global changes, taking into account their mode of reproduction and their dynamics, for the development of restoration strategies and more effective ways of protection.

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