DocumentCode :
3062847
Title :
Local spatial analysis in surface information extraction of coal mining areas with high fractional vegetation cover using multi-source remote sensing data
Author :
Nan Wang ; Chen Du ; Qi Ming Qin
Author_Institution :
Inst. of Remote Sensing & GIS, Peking Univ., Beijing, China
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
2625
Lastpage :
2628
Abstract :
The objective of the study is to utilize the local spatial statistics in multi-source remote sensing to analyze and extract surface anomalies in coal mining areas. We illustrated the equations and characteristics of three local spatial statistics, and then calculated the textual bands of them. In contrast with the selected optimal bands, the local spatial analysis improved the classification accuracy from 93% up to 98% based on Supporting Vector Machine (SVM) Classification. In addition, a few Ground Truth Region of Interests (ROIs) were also derived in the multi-spectral image. By means of the hyper-spectral remotely sensed image covering the ROIs, we directly identified six different surface objects or anomalies and inferred that a clustering of minerals and sandy soil with dense vegetation was a developing coalfield, which should be verified in the ground survey.
Keywords :
coal; geophysical image processing; hyperspectral imaging; image classification; minerals; mining; object detection; soil; statistical analysis; support vector machines; terrain mapping; vegetation; China; SVM classification; Xing Gong coal mining area; coal field; dense vegetation; ground survey; ground truth ROI; ground truth region of interests; high fractional vegetation cover; hyperspectral remotely sensed image; image classification; local spatial analysis; local spatial statistics; mineral clustering; multisource remote sensing data; multispectral image; sandy soil; supporting vector machine; surface anomaly analysis; surface anomaly extraction; surface information extraction; surface objects; textual band; Accuracy; Coal mining; Correlation; Remote sensing; Soil; Support vector machines; Vegetation mapping; Local spatial analysis; Supporting Vector Machine (SVM); coalfield; multi-source remote sensing; surface anomaly extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6723361
Filename :
6723361
Link To Document :
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