Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/522039
Title: Informal settlement extraction from high resolution satellite images using mathematical morphology and deep learning based approaches
Researcher: Prabhu, R
Guide(s): Parvathavarthini
Keywords: Deep Convolutional Neural Network
Grey level co-occurrence matrix
Information And Communication Engineering
University: Anna University
Completed Date: 2023
Abstract: newlineAs the worldwide urban population increases due to the influx of rural newlinemigrants, most cities in emerging nations are confronted with the formation and newlineexpansion of informal settlements. Approximately one-fourth of the global newlineurban population lives in informal settlements at present. Despite the fact that newlinethe expansion of informal settlements affects residential land-use planning, newlineidentification of informal settlements is important for urban analysis, disaster newlinemanagement, land cover change assessment and updating geographical newlinedatabases. But researchers are confronted with challenges in identifying newlineaccurate remote sensing-based informal settlement extraction approaches with newlinereliable and detailed information due to their microstructure, instability and newlinewide variety of textures newline newline
Pagination: xxix,189p.
URI: http://hdl.handle.net/10603/522039
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File25.69 kBAdobe PDFView/Open
02_prelim_pages.pdf1.23 MBAdobe PDFView/Open
03_content.pdf245.87 kBAdobe PDFView/Open
04_abstract.pdf16.78 kBAdobe PDFView/Open
05_chapter 1.pdf2.37 MBAdobe PDFView/Open
06_chapter 2.pdf1.13 MBAdobe PDFView/Open
07_chapter 3.pdf3.25 MBAdobe PDFView/Open
08_chapter 4.pdf1.62 MBAdobe PDFView/Open
09_chapter 5.pdf1.73 MBAdobe PDFView/Open
10_chapter 6.pdf2.46 MBAdobe PDFView/Open
11_annexures.pdf315.32 kBAdobe PDFView/Open
80_recommendation.pdf161.08 kBAdobe PDFView/Open
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