Aiming at the problem that the response speed and the completion time of the task cannot be taken into account at the same time when scheduling tasks in a shared cluster， an adaptive scheduling algorithm based on deadline was proposed. In the algorithm， based on the deadline submitted by the user， the appropriate computing resources were allocated adaptively according to the execution progress of the tasks. Different from that fixed resource parameters were submitted by users in the traditional scheduling methods， in this algorithm， tasks with high priority would be executed with preemptive scheduling under resource constraints. Preemptive scheduling was used to ensure the Quality of Service （QoS）， and additional resources would be allocated to compensate the preempted tasks after the preemption process. The task scheduling experimental results on the Spark platform show that compared with the scheduling algorithm under Yet Another Resource Negotiator （YARN） framework， the proposed algorithm can control the response speed of short tasks strictly and shorten the task completion time of long jobs by 35%.

Concerning that traditional vertical image feature extraction algorithms have poor effect on oblique image matching, a feature extraction algorithm, based on Accelerated KAZE (AKAZE) and Scale Invariant Feature Transform (SIFT) algorithm called AKAZE-SIFT was proposed. Firstly, in order to guarantee the accuracy and distinctiveness of image feature detection, AKAZE operator, which fully preserves the contour information of image, was utilized for feature detection. Secondly, the robust SIFT operator was used to improve the stability of feature description. Thirdly, the rough matching point pairs were determined by the Euclidean distance between object feature point vector and candidate feature point vectors. Finally, the homography constraint was applied to improve the matching purity by random sample consensus algorithm. To evaluate the performance of the feature extraction algorithm, the blur, rotation, brightness, viewpoint and scale changes under the condition of oblique photography were simulated. The experimental results show that compared with SIFT algorithm and AKAZE algorithm, the recall of AKAZE-SIFT is improved by 12.8% and 5.3% respectively, the precision of AKAZE-SIFT is increased by 6.5% and 6.1% respectively, the F1 measure of AKAZE-SIFT is elevated by 13.8% and 5.6% respectively and the efficiency of the proposed algorithm is higher than that of SIFT and slightly worse than that of AKAZE. For the excellent detection and description performance, AKAZE-SIFT algorithm is more suitable for oblique image feature extraction.