scholarly journals Large-scale building height retrieval from single SAR imagery based on bounding box regression networks

2022 ◽  
Vol 184 ◽  
pp. 79-95
Author(s):  
Yao Sun ◽  
Lichao Mou ◽  
Yuanyuan Wang ◽  
Sina Montazeri ◽  
Xiao Xiang Zhu
Keyword(s):  
Large Scale ◽  
Building Height ◽  
Bounding Box ◽  
Sar Imagery

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3472
Author(s):  
Yuming Wei ◽  
Xiaojie Liu ◽  
Chaoying Zhao ◽  
Roberto Tomás ◽  
Zhuo Jiang
Keyword(s):  
Remote Sensing ◽  
Loess Plateau ◽  
High Precision ◽  
Large Scale ◽  
Surface Displacement ◽  
Optical Remote Sensing ◽  
Consolidation Process ◽  
Rapid Urbanization ◽  
Sar Imagery ◽  
The City

Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors” step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than −40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed.

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42 ◽  
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Automatic Segmentation and 3D Printing of A-shaped Manikins using a Bounding Box and Body-feature Points

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Young Jung ◽  
Seonkoo Chee ◽  
In Hwan Sul
Keyword(s):  
3D Printing ◽  
Human Body ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Target Object ◽  
Computational Stability ◽  
Segmentation Strategy ◽  
Bounding Box ◽  
3D Printers ◽  
Scan Data

AbstractA novel algorithm for 3D-printing technology was proposed to generate large-scale objects, especially A-shaped manikins or 3D human body scan data. Most of the conventional 3D printers have a finite printing volume, and it is the users’ work to convert the target object into a printable size. In this study, an automatic three-step segmentation strategy was applied to the raw manikin mesh data until the final pieces had a smaller size than the 3D printer’s maximum printing volume, which is generally called “beam length”. Human body feature point information was adopted for fashion and textile researchers to easily specify the desired cutting positions. A simple bounding box, especially orienting bounding box, and modified Boolean operator were proposed to extract the specified segments with computational stability. The proposed method was applied to graphically synthesized manikin data, and 1/8, 1/4, and 1/2 scale manikins were successfully printed, minimizing the amount of support structure.

scholarly journals IDENTIFICATION OF TEMPORARY SURFACE WATER USING SENTINEL-1 SAR DATA, CASE STUDY: SENTANI FLASH FLOODING, INDONESIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 55-59
Author(s):  
T. A. Gasica ◽  
F. Bioresita ◽  
A. Murtiyoso
Keyword(s):  
Surface Water ◽  
Large Scale ◽  
Flash Flood ◽  
Remote Sensing Data ◽  
Mapping Method ◽  
Flash Flooding ◽  
Rapid Mapping ◽  
Flood Monitoring ◽  
Spatio Temporal ◽  
Sar Imagery

Abstract. Temporary surface water monitoring can provide accurate and reliable information about the spatio-temporal level of surface water. This is very important for various environmental applications, such as flood monitoring. Remote sensing data such as Synthetic Aperture Radar (SAR) is very useful for a large-scale flood monitoring. SAR sensors offer clear advantages by providing their own sources of illumination, thus being able to operate in nearly all-weather/day-night conditions. About 30% disasters which occurred in Indonesia are floods. This hazard has become a recurring disaster that takes place annually. A massive flash flood struck Sentani in the Jayapura Regency in the province of Papua, Indonesia on 16 March 2019, causing 104 deaths. The objective of this work is thus to map temporary surface water (flood) of the Sentani flash flooding event in Indonesia using Sentinel-1 SAR imagery. Sentinel-1 IW GRD and SLC (dual polarimetry) on the event period were used. With two types of Sentinel-1 data, this research produced temporary surface water map using rapid mapping method and SAR polarimetry method. Comparing the results, the similarity of SAR polarimetry method to rapid mapping method is about 39%. Based on reference data, rapid mapping result show better accuracy (82%) than SAR polarimetry method (62%). In addition, processing SLC data needs longer time and higher performance than processing GRD data. Thus, for rapid mapping, it is better to use only Sentinel-1 GRD data.

scholarly journals A High-Resolution Flood Inundation Archive (2016–the Present) from Sentinel-1 SAR Imagery over CONUS

2021 ◽  
pp. 1-40
Author(s):  
Qing Yang ◽  
Xinyi Shen ◽  
Emmanouil N. Anagnostou ◽  
Chongxun Mo ◽  
Jack R. Eggleston ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Weather Conditions ◽  
Automated System ◽  
Flood Inundation ◽  
Spatiotemporal Resolution ◽  
Dynamic Surface ◽  
Critical Success ◽  
Sar Imagery ◽  
Mapping Techniques

AbstractMost existing inundation inventories are based on surveys, news, or passive remote sensing imagery. Affected by spatiotemporal resolution or weather conditions, these inventories are limited in spatial details or coverage. Satellite Synthetic Aperture Radar (SAR) data has recently enabled flood mapping at unprecedented spatiotemporal resolution. However, the bottleneck in producing SAR-based flood maps is the requirement of expert manual processing to maintain acceptable accuracy by most SAR-driven mapping techniques. To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system. RAPID uses U.S. Geological Survey (USGS) water watch system and accumulated precipitation to identify SAR images that potentially overlap a flood event. The dataset includes inundation events with the temporal scale from several days to months. Concluded from all 559 overlapping images in the period from 2016 to the first half of 2019, the comparison of the proposed dataset against the USGS Dynamic Surface Water Extent (DSWE) product yields an overall, user, producer agreements, and critical success index of 99.06%, 87.63%, 91.76%, and 81.23%, respectively, demonstrating the high accuracy of the proposed dataset and the robustness of the automated system. We anticipate this archive to facilitate many applications, including large-scale flood loss and risk assessment, and inundation model calibration and validation.

scholarly journals Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8100
Author(s):  
Bin Yu ◽  
Ming Tang ◽  
Guibo Zhu ◽  
Jinqiao Wang ◽  
Hanqing Lu
Keyword(s):  
Visual Tracking ◽  
Large Scale ◽  
State Of The Art ◽  
Estimation Method ◽  
Target Object ◽  
Reference Information ◽  
Bounding Box ◽  
The Mean ◽  
Modulation Vector ◽  
Initial Target

Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency.

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