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

Abstract

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 κ coefficients show that SBM outperforms the benchmark algorithms.Index Terms-Image segmentation, Markov random fields (MRFs), multispectral imaging, probability density function.

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

“…In the OBIA framework, a segmentation layer is created by an object-generating algorithm in which neighboring image pixels are merged according to spectral, contextual and spatial criteria [6]. As the segmentation step is of significant importance with respect to classification accuracy, appropriate parametrization of the segmentation algorithm is required [7,8]. This parametrization is typically done using supervised, semi supervised or unsupervised techniques [9][10][11][12][13][14][15].…”

Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement

“…Drawing on Markov random field, Baumgartner et al [10] proposed a segmentation method for multi-spectral remote sensing images: the eigenvectors of each frequency band were calculated, the feature parameters of each upper and lower frequency band were estimated, the local smoothing filtering was performed, and the eigenvectors were combined into the segmentation results.…”

Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images

“…For image segmentation we use two methods: an algorithm based on the well-known ML approach and a novel method based on Markov Random Fields (MRF), the SBM algorithm. Both methods have shown good performance on several remote sensing datasets [22], [25].…”

“…For the segmentation stage, two segmentation algorithms are employed: Maximum Likelihood (ML) [21] and Successive Band Merging (SBM) [22]. ML is a simple segmentation technique, whose low computational complexity is suitable for remote sensing imagery.…”