An approach for improving building height estimation from interferometric SAR data

Author(s):  
Giosue Andrey Giardino ◽  
Giovanni Schiavon ◽  
Domenico Solimini
2021 ◽  
Vol 13 (15) ◽  
pp. 2862
Author(s):  
Yakun Xie ◽  
Dejun Feng ◽  
Sifan Xiong ◽  
Jun Zhu ◽  
Yangge Liu

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.


2002 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Olaf Hellwich ◽  
Ivan Laptev ◽  
Helmut Mayer
Keyword(s):  
Sar Data ◽  

2021 ◽  
Author(s):  
Joseph H. Kennedy ◽  
Krik Hogenson ◽  
Andrew Johnston ◽  
Heidi Kristenson ◽  
Alex Lewandowski ◽  
...  

<p>Synthetic Aperture Radar (SAR), with its capability of imaging day or night, ability to penetrate dense cloud cover, and suitability for interferometry, is a robust dataset for event/change monitoring. SAR data can be used to inform decision makers dealing with natural and anthropogenic hazards such as floods, earthquakes, deforestation and glacier movement. However, SAR data has only recently become freely available with global coverage, and requires complex processing with specialized software to generate analysis-ready datasets. Furthermore, processing SAR is often resource-intensive, in terms of computing power and memory, and the sheer volume of data available for processing can be overwhelming. For example, ESA's Sentinel-1 has produced ~10PB of data since launch in 2014. Even subsetting the data to a small scientific area of interest can result in many thousands of scenes, which must be processed into an analysis-ready format.</p><p>The Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3), which is now out of beta and available to the public, provides custom, on-demand processing of Sentinel-1 SAR data at no cost to users. HyP3 is integrated directly into Vertex, ASF's primary data discovery tool, so users can easily select an area of interest on the Earth, find available SAR products, and click a button to send them (individually, or as a batch) to HyP3 for Radiometric Terrain Correction (RTC), Interferometric SAR (InSAR), or Change Detection processing. Processing leverages AWS cloud computing and is done in parallel for rapid product generation. Each process provides options to customize the processing and final output products, and provides metadata-rich, analysis-ready final products to users.</p><p>In addition to the Vertex user interface, HyP3 provides a RESTful API and a python software developers kit (SDK) to allow programmatic access and the ability to build HyP3 into user workflows. HyP3 is open source and designed to allow users to develop new processing plugins or stand up their own custom processing pipeline.</p><p>We will present an overview of using HyP3, both inside Vertex and programmatically, and the available output products. We will demonstrate using HyP3 to investigate the consequences of natural hazards and very briefly discuss the technologies and software design principles used in the development of HyP3 and how users could contribute new plugins, or stand up their own custom processing pipeline.</p>


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