Paper Info
Title
Combining Satellite Images with Feature Indices for Improved Change Detection
Abstract
This paper presents a novel approach to detect changes in satellite images taken from the same location at different timestamps. Different change detection methods are applied to multispectral satellite images taken with the Worldview-2 (WV-2) satellite, as well as to several of their feature indices such as normalized difference vegetation index (NDVI), normalized difference soil index (NDSI), non-homogeneous feature index (NHFD) and red-blue ratio (R/B). Besides, an additional image is used to remove temporary changes like vehicles, persons etc. The combination of changes is computed with a set of pixel-wise operations, and morphological filters are applied to improve the final change map. The combination of the satellite images with their feature indices proved to produce better results than computing the changes independently. This paper summarizes the methodology and presents the results obtained.
Year
DOI
Venue
2018
10.1109/UEMCON.2018.8796538
2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Keywords
Field
DocType
Worldview-2 images,change detection,deep autoencoder,remote sensing
Satellite,Change detection,Normalization (statistics),Computer science,Multispectral image,Remote sensing,Human–computer interaction,Normalized Difference Vegetation Index,Timestamp
Conference
ISBN
Citations 
PageRank 
978-1-5386-7694-3
4
0.45
References 
Authors
0
6
Name
Order
Citations
PageRank
Daniel Pérez192.63
Yan Lu2102.99
Chiman Kwan344071.64
Yuzhong Shen418421.96
Krzysztof Koperski5101.27
jiang li6239.88