Automatic detection of surface water bodies from Sentinel-1 SAR images using Valley-Emphasis method

Abstract

Surface water resource plays as an important role in human daily life and in the eco-environment. In the study Valley-Emphasis method of automatic water extraction was employed to identify surface water bodies at three study areas, having different landscapes and covers, using Sentinel-1A IW images  widely used automated Otsu method was performed for extracting surface water bodies to compare proposed method. The results of proposal method were compared to those of widely used Otsu method and the reference data (e.g. Lansat 7, 8) gave the highest Completeness (User accuracy), Correctness (Producer accuracy) and Quality (Overall accuracies) at 98.8%, 90.7 % and 89.7 %, respectively. The employed method is straightforward, easy to implement and may be applied for other areas even at regional or global scales. The method also improves automatic identification level of surface water bodies, providing essential information for flood disaster research.

References

Otsu, N., 1979: A threshold selection method from gray-level histograms, IEEE Trans Sys, Man Cyber, 9 (1), pp. 62-66.

Hui-Fuang, Ng., 2006: Automatic thresholding for defect detection,” Pattern Recognition Letters, vol. 27,
pp. 1644-1649.

Lun, F. Bo, L., 2012: A modified valley-emphasis method for automatic thresholding, Pattern Recognition Letters,
Volume 33, Issue 6, pp. 703-708.

Small, D., 2011: Flattening gamma: radiometric terrain correction for SAR imagery, IEEE Transactions on Geoscience and Remote Sensing 49, pp. 3081-3093.

Gonzalez, C. Woods, R., 2002: Digital Image Processing, Prentice Hall.

Ye, Z., Mohamadian, H., Ye, Y., 2008: Grey Level Image Processing Using Contrast Enhancement and Watershed Segmentation with Quantitative Evaluation. International Workshop on Content-Based Multimedia Indexing,
pp. 470-475.

Zhang, J., Zheng, J., Cai, J., 2010: A Diffusion Approach To Seeded Image Segmentation,  IEEE Conference On Computer Vision And Pattern Recognition (CVPR), San Francisco, USA, Jun. 13-18, pp. 2125- 2132.

Heipke, C., Mayer, H., Wiedemann, C., Sensing, R., Jamet O., 1997: Evaluation of Automatic Road Extraction. Photogramm. Remote Sens, pp. 47-56.

Zhan, Q., Molenaar, M., Tempfli, K., Shi, W., 2005: Quality assessment for geo-spatial objects derived from remotely sensed data. Int. J. Remote Sens, 26-14, pp. 2953-2974.