Building Change Detection in Myanmar using Image Processing

Ei Phyu Sin Win

doi:10.11594/ijmaber.02.02.11

Abstract

Building change detection makes it is easy to locate buildings from a distance in the sky. They can also observe the development of rural, or urban areas between 10 decade and present. So, higher resolution satellite and aerial pictures are needed to detect buildings. Building shape varies from one to another over the world. Rural areas are sparsely populated, but densely and complexly populated in urban areas. And it is difficult to detect separate buildings from them. To solve obstacles, non-linear filter, line extracting and region thresholding method is used in this research. The test images from the last decade and images of current year are acquired by using google earth pro, and have different spatial resolutions. Detection area is Hlaingthaya Township, Yangon, Myanmar. This system is simulated with MATLAB programming language

Highlights

Google Earth Pro Map provides satellite imagery of 2D and 3D buildings
Detection of buildings that come from these pictures need special attention
Harris corner-based local feature vector (LFV) as Ƙ is the total number of detected Harris feature

Summary

Introduction

Google Earth Pro Map provides satellite imagery of 2D and 3D buildings. The user can fly anywhere on the planet, from the galaxies of space to the bottom of the ocean. Detection of buildings that come from these pictures need special attention. Improvement of strongly and fast building detection algorithms aerial and VHR satellite images are needed. Building Change Detection in Myanmar using Image Processing. EPS Win, 2021 / Building Change Detection in Myanmar using Image Processing detection uses antenna and satellite statue.

Results

Conclusion