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Land surface water mapping is one of the most important remote-sensing applications. However, water areas are spectrally similar and overlapped with shadow, making accurate water extraction from remote-sensing images still a challenging problem. This paper develops a novel water index named as NDWI-MSI, combining a new normalized difference water index (NDWI) and a recently developed morphological shadow index (MSI), to delineate water bodies from eight-band WorldView-2 imagery. The newly available bands (e.g. coastal, yellow, red-edge, and near-infrared 2) of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations. Through our testing, a new NDWI is defined in this study. In addition, MSI, a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas. The NDWI-MSI is created by combining NDWI and MSI, which is able to highlight water bodies and simultaneously suppress shadow areas. In experiments, it is shown that the new water index can achieve better performance than traditional NDWI, and even supervised classifiers, for example, maximum likelihood classifier, and support vector machine.
Xie et al. (Fri,) studied this question.