A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF

Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities such as the normal distribution. In addition, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this letter, we present a new segmentation algorithm that avoids the aforementioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: first, calculate feature vectors for every frequency band; second, estimate contextual parameters for every band and apply local smoothing; and third, merge the feature vectors of the frequency bands to obtain final segmentation. This procedure can be iterated; however, experiments show that after the first iteration, most of the pixels are already in their final state. We call our approach successive band merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the (k) over cap coefficients show that SBM outperforms the benchmark algorithms.