scholarly journals Landslide Mapping Using Multiscale LiDAR Digital Elevation Models

2020 ◽  
Vol 26 (4) ◽  
pp. 405-425
Author(s):  
Javed Miandad ◽  
Margaret M. Darrow ◽  
Michael D. Hendricks ◽  
Ronald P. Daanen
Keyword(s):  
Image Classification ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Support Vector ◽  
Classification Approach ◽  
Resolution Image ◽  
Kappa Value ◽  
The Individual ◽  
Segmentation Image

ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

scholarly journals Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1994
Author(s):  
Qian Ma ◽  
Wenting Han ◽  
Shenjin Huang ◽  
Shide Dong ◽  
Guang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Low Complexity ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Multispectral Images ◽  
High Complexity ◽  
Structure Complexity

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

scholarly journals Fractographic classification in metallic materials by using 3D processing and computer vision techniques

2016 ◽  
Vol 25 (43) ◽  
pp. 83-96
Author(s):  
Maria Ximena Bastidas-Rodríguez ◽  
Flavio A. Prieto-Ortíz ◽  
Édgar Espejo-Mora
Keyword(s):  
Failure Analysis ◽  
Laser Scanning ◽  
Failure Modes ◽  
Confusion Matrix ◽  
Laser Scanning Microscopy ◽  
Confocal Laser ◽  
Future Research ◽  
Support Vector ◽  
3D Vision ◽  
The Individual

Failure analysis aims at collecting information about how and why a failure is produced. The first step in this process is a visual inspection on the flaw surface that will reveal the features, marks, and texture, which characterize each type of fracture. This is generally carried out by personnel with no experience that usually lack the knowledge to do it. This paper proposes a classification method for three kinds of fractures in crystalline materials: brittle, fatigue, and ductile. The method uses 3D vision, and it is expected to support failure analysis. The features used in this work were: i) Haralick’s features and ii) the fractal dimension. These features were applied to 3D images obtained from a confocal laser scanning microscopy Zeiss LSM 700. For the classification, we evaluated two classifiers: Artificial Neural Networks and Support Vector Machine. The performance evaluation was made by extracting four marginal relations from the confusion matrix: accuracy, sensitivity, specificity, and precision, plus three evaluation methods: Receiver Operating Characteristic space, the Individual Classification Success Index, and the Jaccard’s coefficient. Despite the classification percentage obtained by an expert is better than the one obtained with the algorithm, the algorithm achieves a classification percentage near or exceeding the 60 % accuracy for the analyzed failure modes. The results presented here provide a good approach to address future research on texture analysis using 3D data.

scholarly journals Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)

2021 ◽  
Vol 2129 (1) ◽  
pp. 012052
Author(s):  
N Sabri ◽  
H N Abdull Hamed ◽  
M A Isa ◽  
N S Ghazali ◽  
Z Ibrahim
Keyword(s):  
Image Classification ◽  
Food Packaging ◽  
Confusion Matrix ◽  
Primary Phase ◽  
Small Sample ◽  
Human Vision ◽  
Classification Model ◽  
Low Density Polyethylene ◽  
Support Vector ◽  
Low Density

Abstract The motivation of this research is to automate the current food packaging inspection process by implementing the non-destructive approach. The current practices require human intervention where human vision tends to overlook the faulty on the package resulting in accuracy dilemma. Human also may be exhausted due to repeated activities. This paper provides the primary phase for effective automation of image classification solution implemented using Weka software. An evaluation of the performance of the Support Vector Machine (SVM), K-nearest Neighbour (KNN) and Random Forest (RF) classification models for Low-Density Polyethylene (LDPE) food packaging defect image classification using a small sample of dataset and Linear Binary Pattern (LBP) as feature extraction algorithm is investigated. Four criteria have been used to evaluate the performance of each classification model which is accuracy, sensitivity, specificity and precision obtained from the confusion matrix table. The results indicate that SVM performs better than RF and KNN with 95% accuracy, 95% sensitivity, 72% specificity and 95% precision in classifying LDPE food packaging defect images.

scholarly journals Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1486 ◽  
Author(s):  
Asmamaw Gebrehiwot ◽  
Leila Hashemi-Beni ◽  
Gary Thompson ◽  
Parisa Kordjamshidi ◽  
Thomas Langan
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Image Classification ◽  
Urban Areas ◽  
Human Life ◽  
Confusion Matrix ◽  
Support Vector ◽  
Response Planning ◽  
Aerial Vehicles ◽  
Hazardous Area ◽  
Flooded Areas

Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measurements. Unmanned Aerial Vehicles (UAV) technology has recently been recognized as an efficient photogrammetry data acquisition platform to quickly deliver high-resolution imagery because of its cost-effectiveness, ability to fly at lower altitudes, and ability to enter a hazardous area. Different image classification methods including SVM (Support Vector Machine) have been used for flood extent mapping. In recent years, there has been a significant improvement in remote sensing image classification using Convolutional Neural Networks (CNNs). CNNs have demonstrated excellent performance on various tasks including image classification, feature extraction, and segmentation. CNNs can learn features automatically from large datasets through the organization of multi-layers of neurons and have the ability to implement nonlinear decision functions. This study investigates the potential of CNN approaches to extract flooded areas from UAV imagery. A VGG-based fully convolutional network (FCN-16s) was used in this research. The model was fine-tuned and a k-fold cross-validation was applied to estimate the performance of the model on the new UAV imagery dataset. This approach allowed FCN-16s to be trained on the datasets that contained only one hundred training samples, and resulted in a highly accurate classification. Confusion matrix was calculated to estimate the accuracy of the proposed method. The image segmentation results obtained from FCN-16s were compared from the results obtained from FCN-8s, FCN-32s and SVMs. Experimental results showed that the FCNs could extract flooded areas precisely from UAV images compared to the traditional classifiers such as SVMs. The classification accuracy achieved by FCN-16s, FCN-8s, FCN-32s, and SVM for the water class was 97.52%, 97.8%, 94.20% and 89%, respectively.

ML2S-SVM: multi-label least-squares support vector machine classifiers

2019 ◽  
Vol 37 (6) ◽  
pp. 1040-1058 ◽  
Author(s):  
Shuo Xu ◽  
Xin An
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Image Classification ◽  
Classification Problem ◽  
Superior Performance ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Content Type ◽  
Classification Approach

Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

scholarly journals AUTOMATIC OBJECT-ORIENTED ROUNDABOUTS EXTRCTION FROM HIGH RESOLUTION MULTISPECTRAL IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 779-784
Author(s):  
X. Li ◽  
W. Zhang
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Vegetation Index ◽  
Spatial Relationship ◽  
Object Oriented ◽  
Extraction Methods ◽  
Support Vector ◽  
General Rules ◽  
Multispectral Satellite Images ◽  
High Production

Road roundabouts, a typical class of road facilities to avoid collision, are generally not directed extracted in existing road extraction methods. This paper presents a novel four-step approach for automatic vegetated roundabout extractions from high resolution multispectral satellite images, which combines object-oriented extraction, Support Vector Machine (SVM) classification and spatial relationship estimation. Firstly, after proper preconditioning, the vegetated roundabouts are extracted by object-oriented extract algorithm in ENVI with rules that simultaneously taking area, roundness and vegetation index (NDVI) into consideration. After a certain number of experiments, the set of three items’ thresholds can be found, which may stand as the general rules for vegetated roundabouts extraction in similar conditions. Next, the roads are classified using Support Vector Machine (SVM) and the outputs are several band shaped polygons. Then, the holes in road polygons will be detected by examining the topological relation in ArcGIS. Lastly, since the margin of extracted roundabout and the biggest detected hole may not strictly coincide, by comparing the distance between central points of both the extracted roundabout and the hole with the threshold, convincing determination can be made. The proposed automatic approach has been proved to have very high production accuracy that all above 85 % in each case of the test set, which is good enough for automatic vegetated roundabouts extraction from high resolution remote sensing images without manual interpretation.

scholarly journals A New Approach of Ensemble Learning Technique to Resolve the Uncertainties of Paddy Area through Image Classification

2020 ◽  
Vol 12 (21) ◽  
pp. 3666
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
Shih-Chieh Wu ◽  
Hsin-Ping Wang
Keyword(s):  
Image Classification ◽  
Satellite Image ◽  
Image Data ◽  
Support Vector ◽  
Probability Models ◽  
Aerial Photos ◽  
Sensing Technology ◽  
Kappa Value ◽  
Paddy Area

Remote sensing technology has rendered lots of information in agriculture. It has usually been used to monitor paddy growing ecosystems in the past few decades. However, there are uncertainties in data fusion techniques which can be resolved in image classification on paddy rice. In this study, a series of learning concepts integrated by a probability progress Fuzzy Dempster-Shafer (FDS) analysis is presented to upgrade various models and different types of image data which is the goal of this study. More specifically, the study utilized the FDS to generate a series of probability models in the classification of the system. In addition, Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) approaches are employed into the developed FDS system. Furthermore, two different image types are Satellite Image and Aerial Photo used as the analysis material. The overall classification accuracy has been improved to 97.27%, and the kappa value is 0.93. The overall accuracy of the paddy field image classification for a multi-period of mid-scale satellite images is between 85% and 90%. The overall accuracy of the classification using multi-spectral numerical aerial photos can be between 91% and 95%. The FDS improves the accuracy of the above image classification results.

scholarly journals Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products

2020 ◽  
Vol 12 (5) ◽  
pp. 814
Author(s):  
Pedro Vilar ◽  
Tiago G. Morais ◽  
Nuno R. Rodrigues ◽  
Ivo Gama ◽  
Marta L. Monteiro ◽  
...  
Keyword(s):  
Image Classification ◽  
Vegetation Index ◽  
Near Infrared ◽  
Remotely Sensed ◽  
High Yield ◽  
Support Vector ◽  
Surface Model ◽  
Classification Algorithms ◽  
Remotely Sensed Data ◽  
Object Based

Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring using remote sensing (RS). The main hurdle for the remote monitoring of SBP is the fact that the bulk of the pastures are installed in open Montado agroforestry systems. Sparsely distributed trees cast shadows that hinder the identification of the underlaying pasture using Unmanned Aerial Vehicles (UAV) imagery. Image acquisition in the Spring is made difficult by the presence of flowers that mislead the classification algorithms. Here, we tested multiple procedures for the geographical, object-based image classification (GEOBIA) of SBP, aiming to reduce the effects of tree shadows and flowers in open Montado systems. We used remotely sensed data acquired between November 2017 and May 2018 in three Portuguese farms. We used three machine learning supervised classification algorithms: Random Forests (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). We classified SBP based on: (1) a single-period image for the maximum Normalized Difference Vegetation Index (NDVI) epoch in each of the three farms, and (2) multi-temporal image stacking. RF, SVM and ANN were trained using some visible (red, green and blue bands) and near-infrared (NIR) reflectance bands, plus NDVI and a Digital Surface Model (DSM). We obtained high overall accuracy and kappa index (higher than 79% and 0.60, respectively). The RF algorithm had the highest overall accuracy (more than 92%) for all farms. Multitemporal image classification increased the accuracy of the algorithms. as it helped to correctly identify as SBP the areas covered by tree shadows and flower patches, which would be misclassified using single image classification. This study thus established the first workflow for SBP monitoring based on remotely sensed data, suggesting an operational approach for SBP identification. The workflow can be applied to other types of pastures in agroforestry regions to reduce the effects of shadows and flowering in classification problems.

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