Name
Abstract | ||
|---|---|---|
Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11030327 | REMOTE SENSING |
Keywords | Field | DocType |
Sentinel-3,water body mapping,Normalized Difference Water Index (NDWI),sub-pixel mapping,Fuzzy C-means clustering (FCM),Unsupervised | Computer vision,Remote sensing,Artificial intelligence,Pixel,Geology,Water body | Journal |
Volume | Issue | ISSN |
11 | 3 | 2072-4292 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xia Wang | 1 | 62 | 18.50 |
| Feng Ling | 2 | 209 | 21.29 |
| Huaiying Yao | 3 | 0 | 0.34 |
| Yaolin Liu | 4 | 97 | 25.42 |
| Shuna Xu | 5 | 0 | 0.34 |