scholarly journals MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

2016 ◽  
Vol III-7 ◽  
pp. 165-172 ◽  
Author(s):  
Rudolph Joshua Candare ◽  
Michelle Japitana ◽  
James Earl Cubillas ◽  
Cherry Bryan Ramirez
Keyword(s):  
Regularization Parameter ◽  
Optimization Procedure ◽  
Feature Space ◽  
Resource Assessment ◽  
Support Vector ◽  
Lidar Data ◽  
High Resolution Data ◽  
Object Based ◽  
Rule Sets ◽  
Rule Set

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

scholarly journals MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

2016 ◽  
Vol III-7 ◽  
pp. 165-172 ◽  
Author(s):  
Rudolph Joshua Candare ◽  
Michelle Japitana ◽  
James Earl Cubillas ◽  
Cherry Bryan Ramirez
Keyword(s):  
Regularization Parameter ◽  
Optimization Procedure ◽  
Feature Space ◽  
Resource Assessment ◽  
Support Vector ◽  
Lidar Data ◽  
High Resolution Data ◽  
Object Based ◽  
Rule Sets ◽  
Rule Set

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

scholarly journals Properties of Support Vector Machines

1998 ◽  
Vol 10 (4) ◽  
pp. 955-974 ◽  
Author(s):  
Massimiliano Pontil ◽  
Alessandro Verri
Keyword(s):  
Support Vector Machines ◽  
Finite Dimension ◽  
Regularization Parameter ◽  
Feature Space ◽  
Support Vector ◽  
Training Set ◽  
Mathematical Properties ◽  
Support Vectors ◽  
Vector Machines ◽  
Almost All

Support vector machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed support vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this article, we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending on only the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m + 1 margin vectors and observe that m + 1 SVs are usually sufficient to determine the decision surface fully. For relatively small m, this latter result leads to a consistent reduction of the SV number.

scholarly journals OBJECT-ORIENTED CLASSIFICATION OF LIDAR DATA FOR POST-EARTHQUAKE DAMAGE DETECTION

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 421-427
Author(s):  
H. Rastiveis ◽  
N. Khodaverdi zahraee ◽  
A. Jouybari
Keyword(s):  
Damage Detection ◽  
Satellite Image ◽  
Building Damage ◽  
Support Vector ◽  
Lidar Data ◽  
Data Set ◽  
Image Objects ◽  
Object Based ◽  
Training Samples ◽  
Earthquake Damage Detection

<p><strong>Abstract.</strong> The collapse of buildings during the earthquake is a major cause of human casualties. Furthermore, the threat of earthquakes will increase with growing urbanization and millions of people will be vulnerable to earthquakes. Therefore, building damage detection has gained increasing attention from the scientific community. The advent of Light Detection And Ranging (LiDAR) technique makes it possible to detect and assess building damage in the aftermath of earthquake disasters using this data. The purpose of this paper is to propose and implement an object-based approach for mapping destructed buildings after an earthquake using LiDAR data. For this purpose, first, multi-resolution segmentation of post-event LiDAR data is done after building extraction from pre-event building vector map. Then obtained image objects from post-event LiDAR data is located on the pre-event satellite image. After that, appropriate features, which make a better difference between damage and undamaged buildings, are calculated for all the image objects on both data. Finally, appropriate training samples are selected and imported into the object-based support vector machine (SVM) classification technique for detecting the building damage areas. The proposed method was tested on the data set after the 2010 earthquake of Port-au-Prince, Haiti. Quantitative evaluation of results shows the overall accuracy of 92&amp;thinsp;% by this method.</p>

scholarly journals DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 651-656
Author(s):  
F. Samadzadega ◽  
H. Hasani
Keyword(s):  
Urban Area ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Classification Performance ◽  
Feature Subset Selection ◽  
Bees Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Feature Subset

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

scholarly journals Remote Sensing Techniques for Classification and Mapping of Sugarcane Growth

2020 ◽  
Vol 10 (4) ◽  
pp. 6041-6046
Author(s):  
M. K. Villareal ◽  
A. F. Tongco
Keyword(s):  
Remote Sensing ◽  
Saccharum Officinarum ◽  
Support Vector ◽  
Growth Stages ◽  
Remote Sensing Classification ◽  
Object Based ◽  
Sugarcane Yield ◽  
Rule Set ◽  
Remote Sensing Techniques ◽  
Yield Formation

This study aimed to apply remote sensing technologies in delineating sugarcane (Saccharum officinarum) plantations and in identifying its growth stages. Considering the growing demand for sugarcane in the local and global markets, the need for a science-based resource inventory emerges. In this sense, remote sensing techniques’ unique ability is vital to monitor crop growth and estimate crop yield. Object-Based Image Analysis (OBIA) concept was employed by utilizing orthophotos and Light Detection And Ranging (LiDAR) datasets. Specifically, the study applied the Support Vector Machine (SVM) algorithm to generate the resource map, validated by a handheld Global Positioning System (GPS). The classification result showed an accuracy of 98.4%, delineating a total of 13.93 hectares of sugarcane plantation in the study area. The height information from LiDAR datasets aided in developing the rule-set that can further classify the sugarcane according to its growth stages. Results showed that the area distribution of sugarcane at establishment, tillering, yield formation, and ripening stage were 6.65%, 11.61%, 13.89%, and 17.90% respectively. GPS validation points of the growth stages verified the accuracy of SVM. The accuracy results for growth stages, i.e. establishment, tillering, yield formation, and ripening are 88%, 94.4%, 96.3%, and 91.7% respectively. The results proved the usefulness of SVM as a remote sensing classification technique which led to an exact mapping of the sugarcane areas as well as the practical use of LiDAR height information in estimating the growth stages of the mapped resource, both of which can provide valuable aid in estimating the potential sugarcane yield in the future.

scholarly journals Mapping and monitoring of the land use/cover changes in the wider area of Itanos, Crete, using very high resolution EO imagery with specific interest in archaeological sites

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Swaiti Suman
Keyword(s):  
High Resolution ◽  
Image Data ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Large Area ◽  
Open Spaces ◽  
Site Mapping ◽  
Object Based ◽  
Rule Sets ◽  
Rule Set

Archaeological site mapping is important for both understanding the history and protectingthe sites from excavation during developmental activities. As archaeological sites aregenerally spread over a large area, use of high spatial resolution remote sensing imageryis becoming increasingly applicable in the world. The main objective of this study is tomap the land cover of the Itanos area of Crete and of its changes, with specific focus onthe detection of the landscape’s archaeological features. Six satellite images were acquiredfrom the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digitalimagery of two known archaeological sites was used for validation. An object-based imageanalysis classification was subsequently developed using the five acquired satellite images.Two rule sets were created, one using the standard four bands which both satellites haveand another for the two WorldView-2 images with their four extra bands included. Validationof the thematic maps produced from the classification scenarios confirmed a differencein accuracy amongst the five images. Comparing the results of a 4-band rule set versusthe 8-band rule set showed a slight increase in classification accuracy using extra bands.The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separatingthe archaeological sites from the open spaces with little or no vegetation proved tobe challenging. This was mainly due to the high spectral similarity between rocks and thearchaeological ruins. The high resolution of the satellite data allowed for the accuracy indefining larger archaeological sites, but still there was difficulty in distinguishing smallerareas of interest. The digital image data provided a very good 3D representation for thearchaeological sites, assisting as well as in validating the satellite-derived classificationmaps. To conclude, our study provides further evidence that use of high resolution imagerymay allow for archaeological sites to be located, but only where the archaelogical featuresare of an adequate size.

Object-Based Method for Urban Extraction through Using Quick Bird Satellite Imagery, LiDAR Data and Digital Urban Geomatics Techniques

2021 ◽  
Vol 28 (2) ◽  
pp. 1-14
Author(s):  
Noor Hamed
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Urban Area ◽  
Support Vector ◽  
Lidar Data ◽  
Ann Model ◽  
Object Based ◽  
Combining Data ◽  
Artificial Neural

Urban extraction mapping has become increasingly important in recent years and particularity extraction urban features based on remotely sensed data such as highresolution imagery and LiDAR data. Though the researchers used the high spatial resolution image to extract urban area but he methods are still complex and still there are challenges associated with combining data that were acquired over differing time periods using inconsistent standards. So, this study will focus on the extraction of urban area based on an object-based classification method with integration of Quickbird satellite image and digital surface elevation (DSM) extracted from LiDAR data for the Rusafa city of Baghdad, Iraq. All the processes were done in eCognition and ArcGIS software for feature extraction and mapping, respectively. The overall methodological steps proposed in this research for the extraction of urban area using object-based method. In addition of that both the image data and LiDAR-derived DSM were integrated based on the eCognition software for extraction urban map of Rusafa city, Baghdad. Finally, the results indicated that the Artificial Neural Networks (ANN) model achieved the highest training and testing accuracies and performed the best compared to RF and Support Vector Machines (SVM) methods. And also, the results showed that the Artificial Neural Networks (ANN) had capability to extract the boundaries of the buildings and other urban features more accurately than the other two methods. This could be interpreted as the Artificial Neural Networks (ANN) model can learn complex features by the optimization process of the model and its multi-level feature extraction property

scholarly journals Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops

2019 ◽  
Vol 9 (3) ◽  
pp. 4085-4091
Author(s):  
M. K. Villareal ◽  
A. F. Tongco
Keyword(s):  
Learning Algorithm ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Support Vector ◽  
Surface Model ◽  
Lidar Data ◽  
Kappa Index ◽  
Collection Data ◽  
Rule Set ◽  
Five Phases

This study aims to assess the classification accuracy of a novel mapping workflow for sugarcane crops identification that combines light detection and ranging (LiDAR) point clouds and remotely-sensed orthoimages. The combined input data of plant height LiDAR point clouds and multispectral orthoimages were processed using a technique called object-based image analysis (OBIA). The use of multi-source inputs makes the mapping workflow unique and is expected to yield higher accuracy compared to the existing techniques. The multi-source inputs are passed through five phases: data collection, data fusion, image segmentation, accuracy validation, and mapping. Data regarding sugarcane crops were randomly collected in ten sampling sites in the study area. Five out of the ten sampling sites were designated as training sites and the remaining five as validation sites. Normalized digital surface model (nDSM) was created using the LiDAR data. The nDSM was paired with Orthophoto and segmented for feature extraction in OBIA by developing a rule-set in eCognition software. A rule-set was created to classify and to segment sugarcane using nDSM and Orthophoto from the training and validation area sites. A machine learning algorithm called support vector machine (SVM) was used to classify entities in the image. The SVM was constructed using the nDSM. The height parameter nDSM was applied, and the overall accuracy assessment was 98.74% with Kappa index agreement (KIA) 97.47%, while the overall accuracy assessment of sugarcane in the five validation sites were 94.23%, 80.28%, 94.50%, 93.59%, and 93.22%. The results suggest that the mapping workflow of sugarcane crops employing OBIA, LiDAR data, and Orthoimages is attainable. The techniques and process used in this study are potentially useful for the classification and mapping of sugarcane crops.

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