Classification of Urban Feature From Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

Abstract

Abstract:The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs)

“…[50]. Moreover, UAV images can be used for urban feature classification [51], construction project management [45], and construction and demolition waste management [41]. This sector-specific approach not only demonstrates a more precise and efficient application of UAVs but also drives innovations across various domains, expanding the possibilities of their use in different contexts.…”

“…Data processing techniques include the generation of 3D digital models through Structurefrom-Motion (SfM) photogrammetry [41,51] and three-dimensional point clouds [41,49], pixelbased segmentation [49] and classification techniques [51], image segmentation using a Fully Convolutional Network (FCN) [41,49], and deep learning techniques for automatic detection and classification [41,45,48,50,51], such as Support Vector Machine [51] and Siamese Convolutional Networks (SCN). The use of You Only Look Once (YOLO) systems for object recognition [48,50] and Geographic Information Systems (GIS) is also reported [7,37,41].…”