Region-growing segmentation of multispectral high-resolution space images with open software

Most cartographic work is made extracting features from aerial or space images. A first step in this work is segmenting the images in regions that represent, as close as possible, cartographic entities (e.g., roads, buildings, vegetation). Region-Growing segmentation is implemented in a multispectral image using an open source programming language. This segmentation method is analyzed for land used and land cover applications, and it is compared with classification-based segmentation, known as Fuzzy K-Means. Both algorithms, Region Growing and Fuzzy K-Means, are run in an aerial image with four spectral bands (red, green, blue, and near infrared). Depending on the scale, the values of the parameters of the algorithms can yield an under segmentation or over segmentation results. Advantages and disadvantages of both segmentation methods are provided.