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.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

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 GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes

2019 ◽  
Vol 8 (1) ◽  
pp. 46 ◽  
Author(s):  
François Merciol ◽  
Loïc Faucqueur ◽  
Bharath Damodaran ◽  
Pierre-Yves Rémy ◽  
Baudouin Desclée ◽  
...  
Keyword(s):  
High Resolution ◽  
Semantic Content ◽  
Scalable Algorithms ◽  
Large Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range ◽  
Geographic Object ◽  
Monitoring Service ◽  
Hierarchical Representations

Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data.

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 Designing an object based rule set for road detection from high resolution satellite imagery

2015 ◽  
Vol 3 (1) ◽  
pp. 77-94
Author(s):  
Maryam Nikfar ◽  
Mohammad Javad Valadan Zoej ◽  
Mehdi Mokhtarzade ◽  
Mahdi Aliyari Shoorehdeli ◽  
◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Road Detection ◽  
Object Based ◽  
Rule Set ◽  
High Resolution Satellite Imagery

scholarly journals Four-band Thermal Mosaicking: A New Method to Process Infrared Thermal Imagery of Urban Landscapes from UAV Flights

2019 ◽  
Vol 11 (11) ◽  
pp. 1365 ◽  
Author(s):  
Yichen Yang ◽  
Xuhui Lee
Keyword(s):  
High Resolution ◽  
Image Data ◽  
Calibration Method ◽  
Urban Landscapes ◽  
Thermal Camera ◽  
Thermal Images ◽  
Natural Park ◽  
Object Based ◽  
Novel Method ◽  
Thermal Band

Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene.

scholarly journals OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY

2016 ◽  
Vol XLI-B7 ◽  
pp. 183-187 ◽  
Author(s):  
M. Coslu ◽  
N. K. Sonmez ◽  
D. Koc-San
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Digital Data ◽  
Classification Method ◽  
Classification Methods ◽  
Spectral Bands ◽  
Object Based ◽  
Rule Sets ◽  
High Resolution Satellite Imagery

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.

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