Paper Info
Title
Automatic extraction of shadow and non-shadow landslide area from ADS-40 image by stratified classification
Abstract
The objective of this study is fast and accurate to detect the landslides automatically from shadow areas and non- shadow areas that use ADS-40 airborne multispectral image by stratified classification method. First, the shadow area was detected by the brightness method. The shadow and non shadow images were calculated Normalized Difference Vegetation Index (NDVI), and we used iterative self-organizing data analysis technique (ISODATA) unsupervised classification to classify the area of vegetation and non-vegetation. The highest overall classification accuracy of shaded and non-shaded Landslides was 85.75% and 92.75%, respectively. The classification of shaded area by 12-bit image radiation information has a certain capacity. This automated process can be effectively and quickly obtain information of Landslide.
Year
DOI
Venue
2011
10.1109/IGARSS.2011.6049860
IGARSS
Keywords
Field
DocType
shadow,ads-40,geomorphology,landslide,automatic extraction,feature extraction,image classification,geophysical image processing,normalized difference vegetation index,isodata,ads 40 image,nonshadow landslide area,iterative self organizing data analysis technique,vegetation,brightness method,stratified classification,unsupervised classification,brightness,remote sensing,accuracy,histograms
Computer vision,Histogram,Shadow,Computer science,Multispectral image,Remote sensing,Feature extraction,Normalized Difference Vegetation Index,Artificial intelligence,Multispectral pattern recognition,Landslide,Contextual image classification
Conference
ISSN
ISBN
Citations 
2153-6996
978-1-4577-1003-2
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yi-Ta Hsieh102.03
Shou-Tsung Wu200.34
Chen-Sung Liao300.34
Yau-Guang Yui400.34
Jan-Chang Chen500.68
Yuh-Lurng Chung600.68