Considering the fact that feature extraction of infra-red image for burden surface temperature profile in blast furnace is a hard problem，an optimized strategy of threshold selection based on empirical knowledge and information entropy was proposed. Firstly，an image was segmented respectively by the two-peaked histogram method，the Otsu method and the consistency criteria method. Then a criterion to value the image quality based on information entropy was made by rules，which was related to the image sizes of objects. The appropriate threshold value，by which image segmentation was implemented to judge the burden temperature distribution most efficiently，was obtained. Actual application showed that the results of image segmentation were consistent with the expected image features，which satisfied the demands of industrial application and could help operators to judge the blast furnace conditions.