Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Abstract

Extracting surface water distribution with satellite imagery has been an important subject in remote sensing. Spectral indices of water only use information from a limited number of bands, thus they may have poor performance from pixels contaminated by ice/snow, clouds, etc. The detection algorithms using information from all spectral bands, such as constrained energy minimization (CEM), could avoid this problem to some extent. However, these are mostly designed for hyperspectral imagery, and may fail when applied to multispectral data. It has been proved that adding linearly irrelevant data to original data could improve the performance of CEM. In this study, two kinds of linearly irrelevant data are added for water extraction: the spectral indices and the spectral similarity metric data. CEM is designed for targets with low-probability distribution in an image, but water bodies do not always satisfy this condition. We thereby impose a sensible coefficient for each pixel to form the weighted autocorrelation matrix. In this study, the weight is based on the orthogonal subspace projection, so this new method is named Orthogonal subspace projection Weighted CEM (OWCEM). The newly launched Landsat 8 images over two lakes, the Hala Lake in China with ice/snow distributed in the north, and the Huron Lake in OPEN ACCESSWater 2015, 7 795North America, a lake with a very large surface area, are selected to test the accuracy and robustness of our algorithm. The Kappa coefficient and the receiver operating characteristic (ROC) curve are calculated as an accuracy evaluation standard. For both lakes, our method can greatly suppress the background (including ice/snow and clouds) and extract the complete water surface with a high accuracy (Kappa coefficient > 0.96).

Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

“…5). These results confirm firstly its suitability over other band ratio indices on the smallest lakes, in line with results on larger water bodies (Feyisa et al, 2014;Ji et al, 2015;Jiang et al, 2014;Ogilvie et al, 2015). Applied across seven lakes and three images, the method yields a R…”

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

“…However, like other water indices, such as MNDWI and AWEI, WI in Equation (2) is not able to distinguish water from snow/ice [22]. As shown in Figure 1, snow/ice and the mountain shadow pixels covering snow/ice also have higher reflectance in VIS, so their WIs will also be equal to 1.…”

Combined ICESat and CryoSat-2 Altimetry for Accessing Water Level Dynamics of Tibetan Lakes over 2003–2014

“…In order to make them comparable, we first converted ICESat height data to the reference system of CryoSat-2 data. Then, on-lake satellite altimetry footprints were selected via the water mask of Tibetan lakes derived from Landsat image classification [2,23]. The binary water mask, at a resolution of 30 m, is sufficient for identifying altimetry footprints in this study.…”