scholarly journals Introduction to Urban Sensing

2021 ◽  
pp. 311-314
Author(s):  
Wenzhong Shi
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Indoor Positioning ◽  
User Generated Content ◽  
Light Detection And Ranging ◽  
Optical Remote Sensing ◽  
Interferometric Synthetic Aperture Radar ◽  
Wide Range ◽  
Urban Sensing ◽  
Ambient Sensing

AbstractThis chapter overviews the urban sensing technologies for unban informatics to be introduced in the subsequent chapters under Part III of this book. To be covered is a wide range of technologies for urban sensing from the space, the air, the ground, the underground, and on individuals, including optical remote sensing, interferometric synthetic aperture radar, light detection and ranging, photogrammetry, underground sensing, mobile mapping, indoor positioning, ambient sensing, and the use of user-generated content.

scholarly journals Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar

2021 ◽  
pp. 345-365
Author(s):  
Hongyu Liang ◽  
Wenbin Xu ◽  
Xiaoli Ding ◽  
Lei Zhang ◽  
Songbo Wu
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Microwave Radar ◽  
Urban Remote Sensing ◽  
Multi Temporal ◽  
Urban Sensing ◽  
Interferometric Sar ◽  
Aperture Radar

AbstractSynthetic aperture radar (SAR) and interferometric SAR (InSAR) are state-of-the-art radar remote sensing technologies and are very useful for urban remote sensing. The technologies have some very special characteristics compared to optical remote sensing and are especially advantageous in cloudy regions due to the ability of the microwave radar signals used by the current SAR sensors to penetrate clouds. This chapter introduces the basic concepts of SAR, differential InSAR, and multi-temporal InSAR, and their typical applications in urban remote sensing. Examples of applying the various InSAR techniques in generating DEMs and monitoring ground and infrastructure deformation are given. The capabilities and limitations of InSAR techniques in urban remote sensing are briefly discussed.

scholarly journals EVALUASI HASIL INTEGRASI BERBAGAI KETELITIAN DATA MODEL ELEVASI DIGITAL

GEOMATIKA ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 39
Author(s):  
Fanny Zafira Mukti ◽  
Harintaka Harintaka ◽  
Djurdjani Djurdjani
Keyword(s):  
Synthetic Aperture Radar ◽  
Data Model ◽  
Thermal Emission ◽  
Light Detection And Ranging ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Light Detection ◽  
Aster Gdem ◽  
Radio Detection ◽  
Global Data

<p>Data DEM yang dapat diakses dan digunakan dengan gratis antara lain adalah <em>Shuttle Radar Topography</em> <em>Mission </em>(SRTM) dan <em>Advanced Spaceborne Thermal Emission and Reflection Radiometer </em>Global DEM (ASTER GDEM). Kedua data tersebut mencakup seluruh wilayah di Indonesia, namun ketelitian dan resolusinya rendah, serta masih mengandung kesalahan tinggi. Selain data DEM global, data DEM dapat diperoleh dari hasil perekaman sensor <em>Radio Detection and Ranging </em>(RADAR), <em>Light Detection and Ranging</em> (LIDAR), maupun hasil <em>stereoplotting</em> foto udara dan citra satelit. Masing-masing data tersebut memiliki karakteristik seperti terdapatnya <em>pit</em> dan <em>spire</em>, diskontinuitas pada daerah sambungan dan ketelitian data yang bervariasi. Keberagaman karakteristik pada masing-masing sumber data tersebut dapat menyebabkan inkonsistensi nilai ketinggian antar sumber data. Pada penelitian ini dilakukan pembuatan DEM dengan data DTM Rupa Bumi Indonesia (RBI) skala 1:50.000 dan data DTM <em>Interferometric Synthetic Aperture Radar </em>(IFSAR) di Pulau Kalimantan yang dapat mengatasi inkonsistensi ketinggian tersebut. Metode yang digunakan adalah integrasi dan fusi DEM pada mozaik data-data ketinggian. Pada daerah yang bertampalan, dilakukan dua skenario mozaik yaitu mozaik tanpa bobot dan mozaik berbobot. Uji akurasi vertikal dilakukan dengan menggunakan standar Peraturan Kepala BIG Nomor 15 Tahun 2014 tentang Pedoman Teknis Ketelitian Peta Dasar. Penelitian ini menghasilkan mozaik data DTM yang <em>seamless</em> dan <em>smooth</em> menggunakan metode mozaik berbobot dengan akurasi vertikal sebesar 2,065 meter. Hasil mozaik tanpa bobot masih memiliki beberapa daerah yang tidak <em>seamless</em> dan <em>smooth </em>dengan akurasi vertikal sebesar 2,257 meter. Berdasarkan Tabel Ketelitian Geometri Peta RBI dalam PerKa BIG Nomer 15 Tahun 2014, kedua hasil mozaik tersebut masuk dalam skala 1:10.000.</p><p>Kata kunci: model elevasi digital, mozaik, integrasi, fusi DEM</p>

scholarly journals Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet

2018 ◽  
Vol 11 (1) ◽  
pp. 47 ◽  
Author(s):  
Nan Wang ◽  
Bo Li ◽  
Qizhi Xu ◽  
Yonghua Wang
Keyword(s):  
Remote Sensing ◽  
Anomaly Detection ◽  
Superior Performance ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Low Contrast ◽  
Ship Detection ◽  
Wide Range ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

Remote sensing of ocean wave spectra by interferometric synthetic aperture radar

Nature ◽  
1990 ◽  
Vol 345 (6278) ◽  
pp. 793-795 ◽  
Author(s):  
M. Marom ◽  
R. M. Goldstein ◽  
E. B. Thornton ◽  
L. Shemer
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Ocean Wave ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Wave Spectra ◽  
Aperture Radar

scholarly journals Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

2019 ◽  
Vol 26 (2) ◽  
pp. 63
Author(s):  
Desti Ayunda ◽  
Ketut Wikantika ◽  
Dandy A. Novresiandi ◽  
Agung B. Harto ◽  
Riantini Virtriana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Error Matrix ◽  
Tropical Peatland ◽  
Average Value ◽  
Sar Imagery ◽  
Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

scholarly journals ANALYSIS OF PERFORMANCE OF FLAT EARTH PHASE REMOVAL METHODS

2018 ◽  
Vol XLII-5 ◽  
pp. 207-209
Author(s):  
K. Desai ◽  
P. Joshi ◽  
S. Chirakkal ◽  
D. Putrevu ◽  
R. Ghosh
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Paper Analysis ◽  
Phase Unwrapping ◽  
Evaluation Index ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Imaging Geometry ◽  
Analysis Of Performance

<p><strong>Abstract.</strong> Interferometric synthetic aperture radar (InSAR) has been widely used in remote sensing field, which can reflect actual topographic trend or possible surface deformation. Due to the orbit attitude influence, the flat-earth phase usually causes the interferogram dense and difficult to be used in further procedures. Before phase unwrapping, interferogram must be flattened to derive accurate topographic or deformation information. In this paper, analysis of performance of two methods of flat-earth removal is done. First method uses imaging geometry and second method uses precise orbital information. Further, 3-degree, 5-degree and 7-degree polynomials are fitted in the method using precise orbital information. Validation is done both visually and empirically using entropy as the evaluation index.</p>

scholarly journals Fault-Based Geological Lineaments Extraction Using Remote Sensing and GIS—A Review

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 183
Author(s):  
Hemayatullah Ahmadi ◽  
Emrah Pekkan
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Thermal Emission ◽  
Active Faults ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Aperture Radar

Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data sets employed for geological lineament analysis. The critical challenges of this approach and the diverse data verification and validation techniques will be presented. Thus, this review spanned academic articles published since 1975, including expert reports and theses. Landsat series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Sentinel 2 are the prevalent optical remote sensing data widely used for lineament detection. Moreover, Shuttle Radar Topography Mission (SRTM) derived Digital Elevation Model (DEM), Synthetic-aperture radar (SAR), Interferometric synthetic aperture radar (InSAR), and Sentinel 1 are the typical radar remotely sensed data which are widely used for the detection of geological lineaments. The geological lineaments acquired via GIS techniques are not consistent even though a variety of manual, semi-automated, and automated techniques are applied. Therefore, a single method may not provide an accurate lineament distribution and may include artifacts requiring integration of multiple algorithms, e.g., manual and automated algorithms.

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