a. mohsenifar

Image registration is the technique of aligning two or more images with a reference image all acquired at the same geographical area with different acquisition dates, viewpoints, and imaging sensors. This technique is a fundamental process in photogrammetry and remote sensing tasks, and change detection is known as one of its important applications. Urban change detection with an arbitrary time difference between the two images faces limited access to the required datasets. The Google Earth software is known as an appropriate resource to achieve such datasets. Nevertheless, a significant limitation of this software is the co-registration error of the images acquired at different times. In this work, a combined image registration approach has been proposed to cope with this problem. The proposed method is made up of three major steps. In the first step, the KAZE algorithm is applied to extract and describe image features with high stability against illumination distortions. In the second step, the image matching and mismatch elimination procedures are conducted using the VFC (Vector Field Consensus) technique. Finally, in the third step, the geometric relation between the two images is established using the TPS (Thin-Plate Spline) adaptive transformation model. The results derived from the KAZE feature extraction and matching algorithm are compared with those of the SIFT and SURF algorithms in four satellite image pairs related to various urban areas. For datasets 1 to 4, the highest correct correspondence rates of 0.63, 0.81, 0.6, and 0.76 were obtained using the KAZE algorithm compared to the other algorithms. Moreover, the TPS transformation model established the most accurate and reliable geometric relation between the two images with RMSE values of 2.1, 1.8, 2.1, and 1.7 pixels for datasets 1 to 4, respectively. Accordingly, the results indicate the efficacy of the proposed integrated method for robust feature extraction and image registration of the Google Earth imagery with significant brightness and landscape differences.

Change detection is one of the most important processes in photogrammetry and remote sensing, in which occurred changes in a same geographical area are identified over time. Forests are one of the national assets of any country that has a vital role in climate change, groundwater formation and prevention of floods and soil erosion. Thus, an accurate change detection method should be exploited to monitor and maintain forest regions. In this paper, an efficient unsupervised change detection method is proposed for this purpose. Here, two bitemporal sattelite images, acquired at the forest areas of Unitted stasted and Australia are employed  to evaluate the proposed change detection method. In the first step of the proposed approach, discrete wavelet transform was used to generate an efficient change index by fusing of two difference images derived by NDVI and GNDVI vegetation indices. Anisotropic diffusion filtering was then applied to obtain robust change index in which noises was reduced while change regions was highlited. In the next step, the generated index was segmented into changed and unchanged classes using an improved k-means algorithm. Finally, improved MRF model initialed with the initial change map is employed to generate final change map. The proposed MRF model include two novel improvements in the main energy function, resulting in preserveing changed region details. The proposed improved MRF contained superiority of 0.49% and 0.61% compared to traditional MRF in datasets 1 and 2, respectively. The proposed MRF also outperformed Otsu, PCA-kmeans, GAFCM and GMMMRF methods, so that reduced the total error rate by an average of 0.93% and 5.31% in data sets 1 and 2, respectively. In general, the proposed method has a high capability for accurate identifying changes for vegetated areas.

Among sports, soccer is the most popular, spectacular and exciting sport in the world and our country is no exception. Therefore, the aim of this study was to analyze the economic challenges of soccer clubs in Mazandaran. The present study was descriptive-analytic and was applied in terms of objective. The statistical population included university elites, managers, coaches, referees and athletes, economic managers and experts in Mazandaran province and the sample was selected using purposive and convenience sampling methods. In the first stage of the study, economic challenges were identified by interviewing 25 subjects from the statistical population. In the second and third stages, the challenges identified by the Delphi group (25 subjects) were examined; in these two stages, those factors that had at least 60% positive responses remained for the next step. In the final stage, the identified challenges identified in the three stages were ranked using the 5- point Likert questionnaire. Data were analyzed using SPSS and EXCEL software and Dimatel model. The results showed that the lack of transparent rules for privatization was the most effective economic challenge and the lack of spectators’ disapproval of football matches was the least effective economic challenge of Mazandaran football clubs. It is suggested that Iran sport managers and Mazandaran province should make strategic plans in view of the priorities presented in this study and similar studies so that they can ultimately observe the economic growth and development of the football clubs.