A Markov random field model for classification of multisource satellite imagery

Abstract

Abstruct-A general model for multisource classification of remotely sensed data based on Markov Random Fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (Geographic Information Systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GI § crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GI § field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov Random Field models for classification tasks and the encouraging experimental results in our small-scale study, we conclude that the proposed MRF model is useful for classification of multisource satellite imagery.

Jute and Tea Discrimination Through Fusion of Sar and Optical Data

“…Several studies have shown that SAR data may provide information on structural features of the surface complementary to the spectral information in optical data, for instance [3,4,15,17,18]. It has thus been shown, that the discriminating power of SAR images is very much improved when they are used in combination with optical data.…”

Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

“…They provide a flexible tool to include spatial context into image-analysis schemes in terms of minimization of suitable energy functions. While earlier algorithms for optimizing MRF energy, such as iterated conditional modes (ICM) and simulated annealing (Solberg, et al, 1996), (Tarabalka, et al, 2010b) were time consuming, more advanced methods, such as graph cuts ), (Li, et al, 2012) provided powerful alternatives from both theoretical and computational viewpoints, resulting in a growing use of the MRF-based segmentation techniques.…”

“…It iteratively considers every pixel , and assigns a (region or class) label to this pixel, which minimizes the local energy centered at . Solberg, et al (1996) applied the ICM optimization for multisource classification of optical, SAR and geographic information systems images. Farag, et al (2005) employed the ICM method for hyperspectral image classification.…”

Multispectral image fusion for improved RGB representation based on perceptual attributes

“…Pixel-level fusion techniques can also be used to improve the efficiency of classification and detection algorithms. In general, pixel-level fusion methods can be classified into linear methods (Achalakul and Taylor 2001), nonlinear methods (Matsopoulos et al 1994, Matsopoulos and Marshall 1995, Mukhopadhyay and Chanda 2001, optimization techniques (Solberg et al 1996), neural networks (Zhang et al 2001, Shkvarko et al 2001) and image pyramids (Liu et al 2001).…”