Aiming at the issues of low object detection accuracy and false detection of small object in complex background, a modified remote sensing image detection algorithm based on YOLOv8 is proposed. Use of the RFA convolution kernel enhances the extraction of network space features and their associated global information, and further bring in the Deformable Attention mechanism to obtain a more flexible dynamic attention model. The Powerful-IoU loss function is introduced in the regression loss function, which improves the convergence speed of the network and the object detection accuracy. On the RSOD and DOTAv1.0 datasets the mAP of the improved algorithm is increased by 1.5% and 2.3% respectively.
Semi-Global Matching (SGM) algorithm is a conventional method in dense stereo matching and provides an acceptable result. Nevertheless, low accuracy and slow computation speed have been crucial factors restricting the processing of larger images. Meanwhile, similar texture, which appeared enormously on remote sensing images, ordinarily issues in the dilemma of computation failure. In this respect, the paper presents the method of stratifying and precis disparity search space by SGM pyramid and local invariant features to improve computational efficiency, reduce memory footprint and shrink the influence of similar textures, namely RS-rSGM. Experimental results indicate that RS-rSGM can efficiently improve the speed and reduce the time cost of computation on large resolution multi-similarity texture images.
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