Efficient implementation of morphological index for building/shadow extraction from remotely sensed images

Morphological building index (MBI) and morphological shadow index (MSI) are recently developed techniques that aim at automatically detect buildings/shadows using high-resolution remotely sensed imagery. The traditional mathematical morphology operations are usually time-consuming as they are based on the consideration of a wide range of image-object properties, such as brightness, contrast, shapes, sizes, and in the application of series of repeated transformations (e.g., classical opening and closing operators). In the case of MBI and MSI, the computational complexity is also increased due to the use of multiscale and multidirectional morphological operators. In this paper, we provide a computationally efficient implementation of MBI and MSI algorithms which is specifically developed for commodity graphic processing units using NVIDIA CUDA. We perform the evaluation of the parallel version of the algorithms using two different NVIDIA architectures and three widely used hyperspectral data sets. Experimental results show that the computational burden introduced when considering multidirectional morphological operators can be almost completely removed by the developed implementations.