scholarly journals Object-Based High-Rise Building Detection Using Morphological Building Index and Digital Map

2022 ◽  
Vol 14 (2) ◽  
pp. 330
Author(s):  
Sejung Jung ◽  
Kirim Lee ◽  
Won Hee Lee
Keyword(s):  
Large Scale ◽  
Satellite Image ◽  
Majority Voting ◽  
Building Detection ◽  
Digital Map ◽  
High Rise ◽  
Centroid Coordinates ◽  
Object Based ◽  
Multiresolution Segmentation ◽  
Image Object

High-rise buildings (HRBs) as modern and visually unique land use continue to increase due to urbanization. Therefore, large-scale monitoring of HRB is very important for urban planning and environmental protection. This paper performed object-based HRB detection using high-resolution satellite image and digital map. Three study areas were acquired from KOMPSAT-3A, KOMPSAT-3, and WorldView-3, and object-based HRB detection was performed using the direction according to relief displacement by satellite image. Object-based multiresolution segmentation images were generated, focusing on HRB in each satellite image, and then combined with pixel-based building detection results obtained from MBI through majority voting to derive object-based building detection results. After that, to remove objects misdetected by HRB, the direction between HRB in the polygon layer of the digital map HRB and the HRB in the object-based building detection result was calculated. It was confirmed that the direction between the two calculated using the centroid coordinates of each building object converged with the azimuth angle of the satellite image, and results outside the error range were removed from the object-based HRB results. The HRBs in satellite images were defined as reference data, and the performance of the results obtained through the proposed method was analyzed. In addition, to evaluate the efficiency of the proposed technique, it was confirmed that the proposed method provides relatively good performance compared to the results of object-based HRB detection using shadows.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

scholarly journals Development and Validation of a Post-Earthquake Safety Assessment System for High-Rise Buildings Using Acceleration Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1758
Author(s):  
Koji Tsuchimoto ◽  
Yasutaka Narazaki ◽  
Billie F. Spencer
Keyword(s):  
Safety Assessment ◽  
Large Scale ◽  
Model Updating ◽  
Safety Evaluation ◽  
Assessment System ◽  
Shaking Table ◽  
Structural Safety ◽  
High Rise ◽  
Steel Building ◽  
Earthquake Safety

After a major seismic event, structural safety inspections by qualified experts are required prior to reoccupying a building and resuming operation. Such manual inspections are generally performed by teams of two or more experts and are time consuming, labor intensive, subjective in nature, and potentially put the lives of the inspectors in danger. The authors reported previously on the system for a rapid post-earthquake safety assessment of buildings using sparse acceleration data. The proposed framework was demonstrated using simulation of a five-story steel building modeled with three-dimensional nonlinear analysis subjected to historical earthquakes. The results confirmed the potential of the proposed approach for rapid safety evaluation of buildings after seismic events. However, experimental validation on large-scale structures is required prior to field implementation. Moreover, an extension to the assessment of high-rise buildings, such as those commonly used for residences and offices in modern cities, is needed. To this end, a 1/3-scale 18-story experimental steel building tested on the shaking table at E-Defense in Japan is considered. The importance of online model updating of the linear building model used to calculate the Damage Sensitive Features (DSFs) during the operation is also discussed. Experimental results confirm the efficacy of the proposed approach for rapid post-earthquake safety evaluation for high-rise buildings. Finally, a cost-benefit analysis with respect to the number of sensors used is presented.

scholarly journals Combined Block Adjustment for Optical Satellite Stereo Imagery Assisted by Spaceborne SAR and Laser Altimetry Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3062
Author(s):  
Guo Zhang ◽  
Boyang Jiang ◽  
Taoyang Wang ◽  
Yuanxin Ye ◽  
Xin Li
Keyword(s):  
Large Scale ◽  
Satellite Image ◽  
Field Measurements ◽  
High Elevation ◽  
Global Scale ◽  
Stereo Image ◽  
Image Process ◽  
Control Points ◽  
Altimetry Data ◽  
Laser Altimetry

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Alexander Miropolsky ◽  
Anath Fischer
Keyword(s):  
Quality Control ◽  
Computer Model ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Digital Design ◽  
Reconstruction Methods ◽  
Object Based ◽  
Inspection Process ◽  
Important Quality ◽  
Scan Data

The inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its digital design model. Scan data, however, are typically very large scale (i.e., many points), unorganized, noisy, and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for denoising and reduction in scan data by extended geometric filter. The proposed method is applied directly on the scanned points and is automatic, fast, and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.

Prognostic criteria for Polar Lows

2021 ◽  
Author(s):  
Natalia Vazaeva ◽  
Otto Chkhetiani ◽  
Michael Kurgansky
Keyword(s):  
Numerical Simulation ◽  
Wind Velocity ◽  
Large Scale ◽  
Satellite Image ◽  
Development Stage ◽  
Mature Stage ◽  
Initial Development ◽  
Polar Lows ◽  
Prognostic Criteria ◽  
Main Challenge

<p>Polar lows (PLs) are important mesoscale (horizontal diameter up to 1000 km) maritime weather systems at high latitudes, forming pole ward from the polar front. We consider the possible prognostic criteria of PLs, in particular, the kinematic helicity as a quadratic characteristic related to the integral vortex formations and the kinematic vorticity number (KVN). To calculate such characteristics we use reanalysis data and the results of numerical simulation with the WRF-ARW model (Version 4.1.) for the PLs over the Nordic (Norwegian and Barents) seas. For comparison, experimental data are used.</p><p>Our estimate of helicity is based on the connection of an integral helicity (IH) in the Ekman layer with the geostrophic wind velocity, due to the good correlation between IH and half the sum of the wind velocity squared. We have chosen IH averaged over preselected area covering the locality of PLs genesis. This area was moving along with the centre of PL during the numerical simulation.</p><p>The genesis of PLs can be divided into three stages: (i) an initial development stage, in which a number of small vortices appear in a shear zone; (ii) a late development stage, characterized by the merger of vortices; (iii) a mature stage, in which only a single PL is present. Approximately one day before PL formation, a significant increase in helicity was observed. The average helicity bulk density of large-scale motions has values of 0.3 – 0.4 ms<sup>-2</sup>. The local changes in helicity are adjacent to the front side of the PLs. The IH criterion described facilitates the identification of the PLs genesis area. For a more detailed analysis of the PL genesis, it is recommended to apply KVN, which is the additional indicator of PL size and intensity. At the moment of maximum intensity of PLs KVN can reach values of 12 – 14 units. The advantage of using KVN is also in its clear change directly in the centre of the emerging PLs, which allows to precisely indicates the limits of the most intense part of PLs.</p><p>The main challenge is to make the operational forecast of PLs possible through the selection of the prognostic integral characteristics of PLs, sufficient for PLs identification and for analysis of their size and intensity in a convenient, usable and understandable way. The criteria associated with vorticity and helicity are reflected in the PLs genesis and development quite clearly. At this time, such a claim is only a hypothesis, which must be tested using a larger set of cases. Future work will need to extend these analyses to other active PL basins. Also, it would be interesting to compare the representation of PLs by using any other criteria. It is intended to use our combined criteria as a precursor to machine learning-based PLs identification procedure where satellite image analysis and capture of particular cloud patterns are currently applied in most of the cases. It would eliminate the time consuming first stage of collecting data sets.</p><p>This work was supported by the Russian Science Foundation (project No. 19-17-00248).</p>

Object-based image segmentation in photogrammetry for cartographic use

2021 ◽  
Author(s):  
Béla Kovács ◽  
Márton Pál ◽  
Fanni Vörös
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Autonomous Vehicle ◽  
Unsupervised Classification ◽  
Large Set ◽  
Bare Rock ◽  
Object Based ◽  
Social Fund ◽  
Elevation Data ◽  
Control Technologies

<p>The use of aerial photography in topography has started in the first decades of the 20<sup>th</sup> century. Remote sensed data have become indispensable for cartographers and GIS staff when doing large-scale mapping: especially topographic, orienteering and thematic maps. The use of UAVs (unmanned aerial vehicles) for this purpose has also become widespread for some years. Various drones and sensors (RGB, multispectral and hyperspectral) with many specifications are used to capture and process the physical properties of an examined area. In parallel with the development of the hardware, new software solutions are emerging to visualize and analyse photogrammetric material: a large set of algorithms with different approaches are available for image processing.</p><p>Our study focuses on the large-scale topographic mapping of vegetation and land cover. Most traditional analogue and digital maps use these layers either for background or highlighted thematic purposes. We propose to use the theory of OBIA – Object-based Image Analysis to differentiate cover types. This method involves pixels to be grouped into larger polygon units based on either spectral or other variables (e.g. elevation, aspect, curvature in case of DEMs). The neighbours of initial seed points are examined whether they should be added to the region according to the similarity of their attributes. Using OBIA, different land cover types (trees, grass, soils, bare rock surfaces) can be distinguished either with supervised or unsupervised classification – depending on the purposes of the analyst. Our base data were high-resolution RGB and multispectral images (with 5 bands).</p><p>Following this methodology, not only elevation data (e.g. shaded relief or vector contour lines) can be derived from UAV imagery but vector land cover data are available for cartographers and GIS analysts. As the number of distinct land cover groups is free to choose, even quite complex thematic layers can be produced. These layers can serve as subjects of further analyses or for cartographic visualization.</p><p> </p><p>BK is supported by Application Domain Specific Highly Reliable IT Solutions” project  has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.</p><p>MP and FV are supported by EFOP-3.6.3-VEKOP-16-2017-00001: Talent Management in Autonomous Vehicle Control Technologies – The Project is financed by the Hungarian Government and co-financed by the European Social Fund.</p>

scholarly journals Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1193 ◽  
Author(s):  
Yongji Wang ◽  
Qingwen Qi ◽  
Ying Liu
Keyword(s):  
Distance Measures ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
Segmentation Quality ◽  
Two Measures ◽  
Multiresolution Segmentation ◽  
Adjacent Segments

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.

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