Segmentation and morphology of open water bodies from multispectral images

A number of problems in remote sensing require the segmentation of specific spectral classes such as water bodies, clouds or forested areas. Further analysis of these classes may include the calculation of optical reflectance values such as chlorophyll concentration, absolute reflectivity or vegetation indices. To derive reliable measurements of these variables, a precise segmentation 2 from the rest of the image 2 of the spectral classes is needed. In this work, we propose a new methodology to segment open water bodies based on a variant of principal component analysis (PCA). In this variant, information about the spectral class model of the water bodies to be separated in the feature space is required. This information is input by means of a training field encircling a set of pixels representative of this spectral model. A training field for land cover is also defined. This PCA variant produces two sets of multispectral bands, one for water bodies and one for land cover types. The first two bands of each set are input into a fuzzy clustering procedure. By using a merging process, the clusters are merged into two classes: water bodies and the rest of the image. From this, a logic bitmap image is obtained. The pixels of the bitmap consist of ON for water bodies and OFF for the rest of the image. The bitmap is then used to obtain morphological parameters of the water bodies.