Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure最新文献

{"title":"Geo Spatial Analysis for Tsunami Risk Mapping","authors":"A. B. Sambah, F. Miura","doi":"10.5772/INTECHOPEN.82665","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.82665","url":null,"abstract":"Tsunami risk is a combination of the danger posed by tsunami hazard, the vulnerability of people to an event, and the probability of destructive tsunami. The spatial multicriteria approach made a possibility for integrating the vulnerability and risk parameters to assess the potential area that will be affected by the tsunami. The study applied the parameters of physical and social vulnerability and combined element at risk to assess tsunami risk in the coastal area of East Java Indonesia. All parameters in both tsunami vulnerability and tsunami risk assessment were analyzed through cell-based analysis in geographical information system. The weight of each parameter was calculated through the analytical hierarchy process. The results were provided as maps of tsunami vulnerability and tsunami risk. Tsunami risk map described five classes of risk. It described that coastal area with a low elevation and almost flat identified as high risk to the tsunami. The coastal area with a high density of vegetation (mangrove) was defined as the area with low level of tsunami risk. The existence of river and other water canals in coastal area was also analyzed for generating tsunami risk map. Risk map highlights the coastal areas with a strong need for tsunami mitigation plan.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128211875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introductory Chapter: Advanced Ocean Current Simulation from TanDEM Satellite Data","authors":"M. Marghany","doi":"10.5772/INTECHOPEN.84644","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84644","url":null,"abstract":"Satellite microwave data, such as synthetic aperture radar (SAR), have the great potential for retrieving ocean dynamic parameters, for instance, ocean surface current and ocean wave dynamic [1]. One of the attention-grabbing topics is current flow that is needed for short go back satellite cycle and high resolution. These will provide precisely data concerning current dynamic flow [2, 3]. In fact, current is very important for ship navigation, fishing, waste matter substances transport, and sediment transport [4, 5]. Respectively, optical and microwave sensors are enforced to monitor the current flows. Indeed, the ocean surface dynamic options of sea surface current are vital parameters for atmospheric-sea surface interactions. In this regard, the global climate change, marine pollution, and coastal risky are preponderantly dominated by current speed and direction [1]. The measurements of ocean current from space rely on the electromagnetic signal. Truly, associate degree of an electromagnetic signal of optical and microwave reflects from the ocean carrying records concerning one among the first discernible quantities that are the color, the beamy temperature, the roughness, and also the height of the ocean [2]. Recently, the high resolution of SAR sensors such as TerraSar-X, RADARSAT-2, ALOS PALSAR, and the foremost three of the Italian satellite of COSMO-SkyMed have been commenced. Once the four satellites in the COSMO-SkyMed constellation are developed, they are conceivable functioning with a tiny resume time of a little hours [4]. Nevertheless, the initial three of the COSMO-SkyMed, ALOS PALSAR, and RADARSAT-2, satellite data are the cross-track interferometry, which do not allow determining neither coastal water flow nor coastal water level changing. In this regard, the TerraSAR-X satellite data use an along-track interferometric proficiency which simply permits the quantity of sea surface speed. Additionally, phase alterations between the coregistered pixels of an image pair are consistent to Doppler frequency shifts of the signal backscattered and according to line-of-sight velocities of the scatterers. In this view, phase alterations include influences of surface flows and of the dynamic of wave movement. Consequently, the retrieving of tidal current flow can be accurately achieved by both of TerraSAR-X and TanDEM-X. These can be depleted to regulate precisely coastal water height fluctuations. The TerraSAR-X can regulate perfectly the digital surface model (DSM), where depiction of surface-containing topographies exceeds the terrain height, for example, plants and constructions through precision of 2 m. Moreover, TanDEM-X involves dual high-resolution imaging SAR data. In this understanding, both TerraSAR-X and TanDEM-X are hovering in tandem and establishing an enormous radar interferometer with an anticipated competence of creating a comprehensive DSM through a perpendicular resolution of 2 m, exceeding","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"39 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120939961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Dynamic and Static Sensors for Monitoring Infrastructures","authors":"C. Fu, Yifan Zhu, Kuang-yuan Hou","doi":"10.5772/INTECHOPEN.83500","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.83500","url":null,"abstract":"Infrastructures, including bridges, tunnels, sewers, and telecommunications, may be exposed to environmental-induced or traffic-induced deformation and vibrations. Some infrastructures, such as bridges and roadside upright structures, may be sensitive to vibration and displacement where several different types of dynamic and static sensors may be used for their measurement of sensitivity to environmental-induced loads, like wind and earthquake, and traffic-induced loads, such as passing trucks. Remote sensing involves either in situ, on-site, or airborne sensing where in situ sensors, such as strain gauges, displacement transducers, velometers, and accelerometers, are considered conventional but more durable and reliable. With data collected by accelerometers, time histories may be obtained, transformed, and then analyzed to determine their modal frequencies and shapes, while with displacement and strain transducers, structural deflections and internal stress distribution may be measured, respectively. Field tests can be used to char-acterize the dynamic and static properties of the infrastructures and may be further used to show their changes due to damage. Additionally, representative field applications on bridge dynamic testing, seismology, and earthborn/construction vibration are explained. Sensor data can be analyzed to establish the trend and ensure optimal structural health. At the end, five case studies on bridges and industry facilities are demonstrated in this chapter.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114679483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Unmanned Aerial Vehicle for Accurate 3D Imaging","authors":"Y. Kunii","doi":"10.5772/INTECHOPEN.82626","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.82626","url":null,"abstract":"In order to acquire geographical data by aerial photogrammetry, many images should be taken from an aerial vehicle. After that, the images are processed with the help of the structure-from-motion (SfM) technique. Multiple neighboring images with a high rate of overlapping should be obtained for high-accuracy measurement. In the event of natural disasters, UAV operation may sometimes involve risk and should be avoided. Therefore, an easy and convenient method of operating the UAVs is needed. Reports exist on some applications of the UAVs with other devices; however, it will be difficult to prepare a number of such devices in emergency. We considered the most suitable condition for image acquisition by using the UAV. Specifically, some of the altitudes and the rate of overlapping were attempted, and accuracies of the 3D measurement were confirmed. Furthermore, we developed a new camera calibration and measurement method that requires only a few images taken in a simple UAV flight. The UAV in this method was flied vertically and the images were taken at a different altitude. As a result, the plane and height accuracy was (cid:1) 0.093 and (cid:1) 0.166 m, respectively. These values were of higher accuracy than the results of the usual SfM software.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115838626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Feature-Based SAR Image Registration: Appropriate Feature and Retrieval Algorithm","authors":"Dong Li, Yunhua Zhang, Xiaojin Shi","doi":"10.5772/INTECHOPEN.81665","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81665","url":null,"abstract":"An investigation on the appropriate feature and parameter retrieval algorithm is conducted for feature-based registration of synthetic aperture radar (SAR) images. The commonly used features such as tie points, Harris corner, SIFT, and SURF are comprehensively evaluated. SURF is shown to outperform others on criteria such as the geometrical invariance of feature and descriptor, the extraction and matching speed, the localization accuracy, as well as the robustness to decorrelation and speckling. The processing result reveals that SURF has nice flexibility to SAR speckles for the potential relationship between Fast-Hessian detector and refined Lee filter. Moreover, the use of Fast-Hessian to oversampled images with unaltered sampling step helps to improve the registration accuracy to subpixel (i.e., <1 pixel). As for parameter retrieval, the widely used random sample consensus (RANSAC) is inappropriate because it may trap into local occlusion and result in uncertain estimation. An extended fast least trimmed squares (EF-LTS) is proposed, which behaves stable and averagely better than RANSAC. Fitting SURF features with EFLTS is hence suggested for SAR image registration. The nice performance of this scheme is validated on both InSAR and MiniSAR image pairs.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sub-Pixel Technique for Time Series Analysis of Shoreline Changes Based on Multispectral Satellite Imagery","authors":"Qingxiang Liu, J. Trinder","doi":"10.5772/INTECHOPEN.81789","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81789","url":null,"abstract":"The measurement and monitoring of shoreline changes are of great interest to coastal managers and engineers. Shoreline change information can be crucial for the assessment of coastal disasters, design of coastal infrastructure and protection of coastal environment. This chapter presents shoreline change monitoring based on multispectral satellite imagery and sub-pixel technique. Firstly, a brief introduction of shoreline definitions and indicators is given. Sub-pixel techniques for shoreline mapping on multispectral satellite images are then introduced. Following that, a brief review of existing research studies of long-term shoreline change monitoring based on multispectral imagery is given. Subsequently, a case study of sub-pixel shoreline change monitoring at the northern Gold Coast on the east coast of Australia is presented. By comparing the longshore averaged beach widths at seven representative transects from Landsat with those from Argus imaging data, the RMSEs range from 9.1 to 12.3 m and the correlations are all no less than 0.7. Annual means and variabilities of beach widths were estimated without significant differences from the reference data for most of the results. Finally, conclusions and recommendations for future work are given.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129022671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification","authors":"Chang Luo, Hanqiao Huang, Yong Wang, Shiqiang Wang","doi":"10.5772/INTECHOPEN.81982","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81982","url":null,"abstract":"Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for the task of remote scene classification, there are no sufficient images to train a very deep CNN from scratch. Instead, transferring successful pre-trained deep CNNs to remote sensing tasks provides an effective solution. Firstly, from the viewpoint of generalization power, we try to find whether deep CNNs need to be deep when applied for remote scene classification. Then, the pre-trained deep CNNs with fixed parameters are transferred for remote scene classification, which solve the problem of time-consuming and parameters over-fitting at the same time. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in unsupervised setting. This chapter also provides baseline for applying deep CNNs to other remote sensing tasks.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130209370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis","authors":"R. Natsuaki","doi":"10.5772/INTECHOPEN.81465","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81465","url":null,"abstract":"Synthetic aperture radar (SAR) has become a major tool for disaster monitoring. Its all-weather capability enables us to monitor the affected area soon after the event happens. Since the first launch of spaceborne SAR, its amplitude images have been widely used for disaster observations. Nowadays, an accurate orbit control and scheduled frequent observations enable us to perform interferometric analysis of SAR (InSAR) and the use of interferometric coherence. Especially for L-band SAR, its long-lasting temporal coherence is an advantage to perform precise interferometric coherence analysis. In addition, recent high resolution SAR images are found to be useful for observing relatively small targets, e.g., individual buildings and facilities. In this chapter, we present basic theory of SAR observation, interferometric coherence analysis for the disaster monitoring, and its examples for the harbor facilities. In the actual case, DInSAR measurement could measure the subsidence of the quay wall with 3 cm error.","PeriodicalId":308924,"journal":{"name":"Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125096412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}