River Detection in Remotely Sensed Imagery Using Gabor Filtering and Path Opening

Remote Sensing

Kang Yang¹  Manchun Li¹  Yongxue Liu¹  Liang Cheng¹  Qiuhao Huang¹  Yangming Chen¹  Deepak R. Mishra²  Eurico J. D’Sa²  Sachidananda Mishra² 

[1] Department of Geographic Information Science, Nanjing University, Nanjing, Jiangsu 210023, China; E-Mails:Department of Geographic Information Science, Nanjing University, Nanjing, Jiangsu 210023, China;

关键词: river detection;    Gabor filter;    path opening;    Landsat 8;   

DOI  :  10.3390/rs70708779

来源: mdpi

【 摘 要 】

Detecting rivers from remotely sensed imagery is an initial yet important step in space-based river studies. This paper proposes an automatic approach to enhance and detect complete river networks. The main contribution of this work is the characterization of rivers according to their Gaussian-like cross-sections and longitudinal continuity. A Gabor filter was first employed to enhance river cross-sections. Rivers are better discerned from the image background after filtering but they can be easily corrupted owing to significant gray variations along river courses. Path opening, a flexible morphological operator, was then used to lengthen the river channel continuity and suppress noise. Rivers were consistently discerned from the image background after these two-step processes. Finally, a global threshold was automatically determined and applied to create binary river masks. River networks of the Yukon Basin and the Greenland Ice Sheet were successfully detected in two Landsat 8 OLI panchromatic images using the proposed method, yielding a high accuracy (~97.79%), a high true positive rate (~94.33%), and a low false positive rate (~1.76%). Furthermore, experimental tests validated the high capability of the proposed method to preserve river network continuity.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.