The scattering measurements of individual pixels in polarimetric SAR images are affected by speckle; hence, the performance of classification approaches, taking individual pixels as elements, would be damaged. By introducing the spatial relation between adjacent pixels, a novel classification method, taking regions as elements, is proposed using a Markov random field (MRF). In this method, an image is oversegmented into a large amount of rectangular regions first. Then, to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, a Wishart MRF model, combining the Wishart distribution with the MRF, is proposed, and an iterative conditional mode algorithm is adopted to adjust oversegmentation results so that the shapes of all regions match the ground truth better. Finally, a Wishart-based maximum likelihood, based on regions, is used to obtain a classification map. Real polarimetric images are used in experiments. Compared with the other three frequently used methods, higher accuracy is observed, and classification maps are in better agreement with the initial ground maps, using the proposed method.

Dockerfile plays an important role in the Docker-based software development process, but many Dockerfile codes are infected with smells in practice. Understanding the occurrence of Dockerfile smells in open-source software can benefit the practice of Dockerfile and enhance project maintenance. In this paper, we perform an empirical study on a large dataset of 6,334 projects to help developers gain some insights into the occurrence of Dockerfile smells, including its coverage, distribution, co-occurrence, and correlation with project characteristics. Our results show that smells are very common in Dockerfile codes and there exists co-occurrence between different types of Dockerfile smells. Further, using linear regression analysis, when controlled for various variables, we statistically identify and quantify the relationships between Dockerfile smells occurrence and project characteristics. We also provide a rich resource of implications for software practitioners.

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Information contained in fully polarimetric SAR data is plentiful. How to exploit the information to improve accuracy is important in segmentation of fully polarimetric SAR images. Several frequently used feature vectors and methods are investigated, and a novel method is proposed for segmenting multi-look fully polarimetric SAR images in this paper, starting from the statistical characteristic and the interaction between adjacent pixels. In order to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, the Wishart distribution of the covariance matrix is integrated with the Markov random field, then the iterated conditional modes (ICM) is taken to implement the maximum a posteriori estimation of pixel labels. Although the ICM has good robustness and fast convergence, it is affected easily by initial conditions, so the Wishart-based ML is used to obtain the initial segmentation map, in order to exploit completely the statistical a priori knowledge in the initial segmentation step. Using multi-look fully polarimetric SAR images, acquired by the NASA/JPL AIRSAR sensor, the new approach is compared with several other commonly used ones, better segmentation performance and higher accuracy are observed.