PERFORMANCE EVALUATION OF THERMOGRAPHIC CAMERAS FOR PHOTOGRAMMETRIC DOCUMENTATION OF HISTORICAL BUILDINGS

Thermographic cameras record temperatures emitted by objects in the infraredregion. These thermal images can be used for texture analysis and deformationcaused by moisture and isolation problems. For accurate geometric survey of thedeformations, the geometric calibration and performance evaluation of thethermographic camera should be conducted properly. In this study, an approach isproposed for the geometric calibration of the thermal cameras for the geometricsurvey of deformation caused by moisture. A 3D test object was designed and usedfor the geometric calibration and performance evaluation. The geometric calibrationparameters, including focal length, position of principal point, and radial andtangential distortions, were determined for both the thermographic and the digitalcamera. The digital image rectification performance of the thermographic camerawas tested for photogrammetric documentation of deformation caused by moisture.The obtained results from the thermographic camera were compared with the resultsfrom digital camera based on the experimental investigation performed on a studyarea

AUTOMATIC BUILDING EXTRACTION USING LiDAR AND AERIAL PHOTOGRAPHS

ABSTRACT This paper presents an automatic building extraction approach using LiDAR data and aerial photographs from a multi-sensor system positioned at the same platform. The automatic building extraction approach consists of segmentation, analysis and classification steps based on object-based image analysis. The chessboard, contrast split and multi-resolution segmentation methods were used in the segmentation step. The determined object primitives in segmentation, such as scale parameter, shape, completeness, brightness, and statistical parameters, were used to determine threshold values for classification in the analysis step. The rule-based classification was carried out with defined decision rules based on determined object primitives and fuzzy rules. In this  study, hierarchical classification was preferred. First, the vegetation and ground classes were generated; the building class was then extracted. The NDVI, slope and Hough images were generated and used to avoid confusing the building class with other classes. The intensity images generated from the LiDAR data and morphological operations were utilized to improve the accuracy of the building class. The proposed approach achieved an overall accuracy of approximately 93% for the target class in a suburban neighborhood, which was the study area. Moreover, completeness (96.73%) and correctness (95.02%) analyses were performed by comparing the automatically extracted buildings and reference data.

The Use of Machine Learning Algorithms in Urban Tree Species Classification

Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species

The Use of Machine Learning Algorithms in Urban Tree Species Classification

Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species

Automatic building extraction using LiDAR and aerial photographs

This paper presents an automatic building extraction approach using LiDAR data and aerial photographs from a multi-sensor system positioned at the same platform. The automatic building extraction approach consists of segmentation, analysis and classification steps based on object-based image analysis. The chessboard, contrast split and multi-resolution segmentation methods were used in the segmentation step. The determined object primitives in segmentation, such as scale parameter, shape, completeness, brightness, and statistical parameters, were used to determine threshold values for classification in the analysis step. The rule-based classification was carried out with defined decision rules based on determined object primitives and fuzzy rules. In this study, hierarchical classification was preferred. First, the vegetation and ground classes were generated; the building class was then extracted. The NDVI, slope and Hough images were generated and used to avoid confusing the building class with other classes. The intensity images generated from the LiDAR data and morphological operations were utilized to improve the accuracy of the building class. The proposed approach achieved an overall accuracy of approximately 93% for the target class in a suburban neighborhood, which was the study area. Moreover, completeness (96.73%) and correctness (95.02%) analyses were performed by comparing the automatically extracted buildings and reference data

Performance evaluation of thermographic cameras for photogrammetric documentation of historical buildings

Thermographic cameras record temperatures emitted by objects in the infrared region. These thermal images can be used for texture analysis and deformation caused by moisture and isolation problems. For accurate geometric survey of the deformations, the geometric calibration and performance evaluation of the thermographic camera should be conducted properly. In this study, an approach is proposed for the geometric calibration of the thermal cameras for the geometric survey of deformation caused by moisture. A 3D test object was designed and used for the geometric calibration and performance evaluation. The geometric calibration parameters, including focal length, position of principal point, and radial and tangential distortions, were determined for both the thermographic and the digital camera. The digital image rectification performance of the thermographic camera was tested for photogrammetric documentation of deformation caused by moisture. The obtained results from the thermographic camera were compared with the results from digital camera based on the experimental investigation performed on a study area