Automatic extraction of shadow and non-shadow landslide area from ADS-40 image by stratified classification

Author

Hsieh, Yi-Ta ; Wu, Shou-Tsung ; Liao, Chen-Sung ; Yui, Yau-Guang ; Chen, Jan-Chang ; Chung, Yuh-Lurng

Author_Institution

Dept. of Grad. Inst. of Bioresources, Nat. Pingtung Univ. of Sci. & Technol., Pingtung, Taiwan

fYear

2011

fDate

24-29 July 2011

Firstpage

3050

Lastpage

3053

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.

Keywords

feature extraction; geomorphology; geophysical image processing; image classification; vegetation; ADS 40 image; ISODATA; Normalized Difference Vegetation Index; automatic extraction; brightness method; iterative self organizing data analysis technique; nonshadow landslide area; stratified classification; unsupervised classification; vegetation; Accuracy; Brightness; Educational institutions; Histograms; Remote sensing; Terrain factors; Vegetation mapping; ADS-40; landslide; shadow; stratified classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6049860

Filename

6049860