Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorithm based on homography transformation was proposed. The image sequences were dynamically divided into framegroups according the data from airborne inertial navigation systems, and the intermediate frames in the same frame-group was b i-directionally predicted by the first-frame and the endframe with homography transformation. The homography matrix was got approximately by the airborne inertial navigation systems firstly and then was accurately computed by fast multiple sub-areas template matching. At the end the first frame and the residual images of the intermediate frames of the same frame-group was merged into a big image and coded by JPEG2000 to generate fixed-size code streams. The experiment results show that the proposed algorithm was with high compression performance, low computational complexity and excellent capacity for code-size control and will has good prospect in engineer.
Ship detection in remote sensing has been achieving increasing significance recently. In remote sensing, ships are arbitrary oriented and the detector has to learn the object features of arbitrary orientation by rote, which demands a large amount of training data to prevent overfitting. In addition, plenty of ships have a distinct direction from the center point to the head point. However, little attention has been paid to the direction information of ships and previous studies just predict the bow directions of ships. In this paper, we propose to further exploit the ship direction information to solve the arbitrary orientation problem, including direction augmentation, direction prediction, and direction normalization. A Variable-Direction Rotated RoI Align module is designed for direction augmentation and normalization with an additional feature extraction direction as input. The direction augmentation method directly augments the features of ship RRoIs and brings great diversities to the training data set. The direction prediction introduces additional direction information for learning and helps to reduce noise. In the direction normalization method, the predicted ship directions are utilized to normalize the directions of ship features from stern to bow through the VDR RoI Align module, making the ship features present in one orientation and easier to be identified by the detector. On the L1 task of the HRSC2016 data set, the direction augmentation method and direction normalization method boost the RoI Transformer baseline from 86.2% to 90.4% and 90.6%, respectively, achieving the state-of-the-art performance.
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