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Advancements and Challenges in Business Applications of SAR Images
| Content Provider | Scilit |
|---|---|
| Author | Kaushik, Prachi Jabin, Suraiya |
| Copyright Year | 2021 |
| Description | Remote sensing technology benefits various types of studies such as natural disasters, climate change, soil monitoring, temporal changes, deforestation, land degradation, urban growth, etc. over a period of time. The Synthetic aperture radar satellites capture high-resolution images as big data for the geospatial monitoring of land and sea surfaces. This data is not limited by the cloud cover or rain. The image data is huge in terms of volume as each image data product is in gigabytes (GB) which gives the challenge to handle and process the volume of data. The SAR software is evolving to handle the huge volume of data and run automated analytical workflows at a scale level to solve complex real-life applications. The velocity of the SAR data ingestion is high to enable the users to access the data for scientific and business problems across a wide area of disciplines including disaster preparation and response, land/sea monitoring, land cover classification, and change detection. Big data analytics, deep learning algorithms, and cloud infrastructure including GPUs have enabled small and big level industrial applications of remote sensing data. The thematic and disaster response maps prepared after the SAR analysis can aid the government, non-government, business-specific needs in crucial decision making. Recent advances in machine learning, deep learning, and artificial intelligence have opened a wide door to convert satellite data into actionable information. This chapter explores wide applications of SAR satellite images influencing global, local, industries over a variety of landforms. It gives a timescale overview of the earth's surface changing on a day-to-day and hour to hour by time series analysis and SAR interferometry. In this chapter, we present recent trends in business applications of SAR images using deep learning toward environmental monitoring and natural disasters. Book Name: Big Data Analytics |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003175711-6&type=chapterpdf |
| DOI | 10.1201/9781003175711-6 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-11-29 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Big Data Analytics Decision Making Big Data Business Applications Natural Disasters Cloud Sar Images Satellite |
| Content Type | Text |
| Resource Type | Chapter |
