The effects of CEC passes, isothermal holding time and reheating temperature on the microstructure evolution and grain coarsening behaviour of AZ61 magnesium alloy produced by the recrystallisation and partialmelting (RAP) process were investigated. Before partial remelting, as-cast AZ61 alloy was deformed by cyclic extrusion compression (CEC) with one pass and two pass at 330 • C. After CEC, the microstructure consisted of unrecrystallized grains and deformed eutectic compounds. Increasing isothermal holding time resulted in the formation of spheroidal grains surrounded by liquid films. With increasing the isothermal holding time, the solid grain size increased and the degree of spheroidization was improved. With increasing the reheating temperature, namely increasing liquid fraction, the solid grain size obviously decreased during the period from 560 • C to 570 • C and then slightly increased after 570 • C, while the shape factor increased monotonously. During partial remelting, increasing reheating temperature can properly short the isothermal holding time to obtain fine structure. Moreover, increasing the numbers of CEC passes could produce finer semi-solid microstructure. The coarsening behavior of solid grains in the semi-solid state obeys Ostwald ripening and grain coalescence mechanisms. The coarsening rate constant, K, was 80 µm 3•s −1 for samples partially remelted at 595 • C. After CEC plus partial remelting, the ideal and fine semi-solid state structure can be obtained, which was suitable for thixoforming.
Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.
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